As we enter 2025, career aspirations are part of common new-year goals. Whether one is looking for a job switch or preparing for their own venture, understanding the latest hiring trends is essential to stay competitive for both sought-after talent and recruiters seeking the best fit.

A recent report from Naukri.com identified industries fueling job market expansion in December 2024. As per the report, AI and machine learning (ML) topped the charts with a 36% growth, followed by oil and gas with over 13% growth, FMCG with over 12% growth, and healthcare with over 12% growth. 

Pawan Goyal, chief business officer at Naukri, said, “India’s job market is entering 2025 with vigour, driven by AI/ML growth and creative sectors. The surge in fresher hiring and evolving C-suite roles signals a transformation into a more dynamic landscape.”

Skills Critical for Hiring

AI skills are emerging as a key requirement in this evolving job market. A LinkedIn report revealed that employees skilled in generative AI are five times more likely to develop complementary skills like creative ideation, design thinking, and emotional intelligence. 

Organisations with more GenAI-skilled employees also see four times the usual rate of leadership promotions and five times the usual rate of overall promotions.

In an interview with AIM, Joseph Sudheer Thumma, CEO and MD of IT company Magellanic Cloud, stressed the growing importance of AI expertise. “In today’s era, AI-related skills are essential across roles. At Magellanic Cloud, we prioritise candidates with a proven ability to learn quickly and adapt to change. While technical AI knowledge is crucial for some roles, we value curiosity and a growth mindset, ensuring continuous upskilling to meet tech-driven challenges.”

Echoing this sentiment, Rajesh Chandran Sogasu, senior VP at IT firm Happiest Minds Technologies, added, “AI skills are vital for our Generative AI Business Unit and Analytics/AI Center of Excellence. For other roles, we offer training to help employees leverage AI for career growth.”

In 2025, professionals with expertise in AI/ML technologies such as Python, R, TensorFlow, PyTorch, and scikit-learn will be highly sought after, along with the knowledge of neural networks and natural language processing (NLP). 

Data engineering and big data skills, including experience in data pipelines, Hadoop, Spark, and cloud platforms like AWS and Google Cloud are also in demand. Organisations are actively seeking data science and analytics capabilities, such as advanced statistical analysis, SQL proficiency, and familiarity with visualisation tools like Tableau and Power BI. 

The growing focus on AI ethics underscores the importance of understanding AI governance, bias detection, and data privacy laws. Additionally, roles in computer vision require expertise in OpenCV (Open Source Computer Vision Library), deep learning for vision tasks, and 3D modelling tools. At the same time, AI product management demands strategic thinking, project management skills, and a strong grasp of AI technologies.

Appraisal Migration Wave

The IT sector’s annual migration season kicks off in April. According to placement data from the job portal Apna.co, 82% of professionals are planning to change jobs, creating a ripple of anxiety among HR departments.

Speaking to AIM, Khushbu Singh, lead process excellence at Capgemini, explained an unusual shift this year, “Typically, most companies follow the April-June appraisal cycle. However, delays in appraisals from the October-November-December (OND) cycle are now spilling into February. If OND cycles extend, it’s likely the April-June cycle will push into September. Additionally, companies consolidating two appraisal cycles into one are adding to this uncertainty.”

What’s Next?

As AI continues to reshape industries like IT, retail, automotive, and healthcare, Bengaluru stands out as the hub for AI jobs in India. 

V Suresh, CEO of foundit, told AIM, “AI-driven solutions are creating demand for roles like machine learning engineers, AI researchers, and automation specialists. Key skills include NLP, computer vision, and AI ethics. Opportunities are concentrated in senior roles, with Bengaluru standing out as India’s AI job capital.”

Beyond Bengaluru, other cities are also making strides. Chennai saw a 35% growth, while Hyderabad posted a 15% growth, driven by demand in IT, consumer durables, and real estate. Coimbatore stood out, with fresher hiring growing by 14%, fueled by a remarkable 52% surge in foreign MNC hiring.

So, upskilling is no longer optional. Updating resumes, staying informed about industry shifts, and being ready to relocate, if necessary, will be imperative this year.

The post Hiring Trends in 2025 appeared first on Analytics India Magazine.

In the past few years, Indian IT has been heavily criticised for extending offer letters to freshers and then delaying onboarding. In certain cases, these giants rescinded their offer, citing potential cost-cutting measures, with the rise of generative AI adoption being named a key factor. Cut to this year, not a lot has changed. 

In a rant on Reddit, a developer alleged that LTIMindtree was not giving out joining letters to the freshers of the 2024 batch. “Instead of on-boarding us, they are hiring for 2025. I’ve been waiting for the last two months for the offer letter,” said the developer.

He added that there is no information on the joining date, and only a handful of them got onboarded. Nodding to this, developers have been flocking to the discussion. “I had an offer for ’23 graduate, November as joining, they delayed it till May 2024, shamelessly asking me to join their internship program first for 2 weeks. If I clear the exam, I get the placement,” said a software developer.

Companies like Cognizant and TCS have also been accused of following this practice. “…Especially with their CSD training. They wasted our 6 months and then assigned us to a completely different domain and a support project,” said a developer about Cognizant.

Vouching for this, another developer, who currently works for the organisation, said that they were promised developer roles but ended up in tech support. “This organisation is just filled with support projects. Close to 95% of the graduates from my training batch were put into support,” they added, ruing that even the skills do not seem to matter much here.

“Waiting for my Capgemini offer letter from 2020,” another developer chipped in.

What was the Promise?

In October 2022, Indian IT biggies, such as Wipro, Infosys, and Tech Mahindra, had delayed the onboarding for months and were all revoking offer letters, affecting nearly 30,000 freshers. Indian IT firms had largely stopped hiring freshers in 2023. 

Adding insult to injury, the “hired” freshers, who were waiting endlessly to receive their joining letters, were blamed for not meeting the qualification criteria for the job role. This trend was expected to continue this year, along with rampant possible layoffs. Though the layoffs didn’t happen, the hiring has definitely stopped.

However, in their latest earnings call, Indian IT giants have promised that they would continue reducing their bench size and also hire freshers to increase the headcount. For example, Infosys announced that it is on track to onboard 15,000 to 20,000 freshers at the group level in FY25, though it did not break down the numbers between the current and previous years. All freshers will be onboarded within that range.

Similarly, HCLTech said that it has added 2,932 freshers during its latest Q2 FY25 results. “We had added about 12,000 freshers in FY24 when the campus hiring across the sector was very muted,” said C Vijaykumar, the CEO of HCLTech, adding that though the headcount in total has declined, the wage bill has gone up as they are restructuring the pyramid with specialised skills. 

Though there was no mention of the hiring of freshers in its latest earnings call in August, Cognizant was offering an INR 2.5 LPA salary for freshers. Though it was later said that the figure was just for the interns, the offering was still very low and matched exactly with the package offered decades ago in 2002.

As for TCS, Milind Lakkad, the chief human resource officer at TCS, said during Q2 FY25 call that it welcomed 11,000 associates in the first half of the year, and the company remains on track for trainee onboarding as planned. “We have also commenced the campus hiring process for FY26,” he added.

Reportedly, in July this year, TCS had 80,000 job openings which it was unable to fill citing skills issues among the graduates.

What to Do?

This problem of delayed hiring has been an ongoing one for years. The developers have figured out that applying for any WITCH (Wipro, Infosys, TCS, Cognizant, HCLTech) job means applying to 10 other jobs at the same time as they would have to wait for months before actually getting any response. “My joining letter came 8 months later when I was a fresher for Infosys, I’ve lost almost a year of experience I could’ve had. The best option is to keep applying to other companies,” said a developer.

Some developers have waited for two years after having sat for interviews for the Ninja role at TCS. Instead, they could have gathered two years of experience elsewhere and applied for better jobs.  “Many of my friends who went into WITCH companies are now on the bench with uncertainty around whether or not a project will be assigned to them,” shared another software engineer.

It’s time Indian IT paid attention to really fix its broken hiring process.

The post No One Gets Named and Shamed Like Indian IT Hiring appeared first on Analytics India Magazine.

Not only is AI increasingly gaining traction, but automation and AI agents, along with various other technologies, are also being adopted across industries. Now, LinkedIn, a top choice for recruiters and candidates, has launched an AI agent, a hiring assistant, to free recruiters from repetitive work and allow them to focus on important tasks like advising hiring managers, connecting with candidates, and creating a pleasant candidate experience.

“Hiring Assistant is in the hands of our own recruiters and available in the charter to a select group of LinkedIn customers, including AMD, Canva, Siemens, and Zurich Insurance,” said Hari Srinivasan, VP of Product at LinkedIn.

According to Srinivasan, 55% of HR professionals globally feel expectations from them at work are higher than ever before, while 42% feel overwhelmed by how many decisions they have to make each day.

Now, the recruiters can delegate time-consuming tasks like finding candidates and assisting in applicant review to the Hiring Assistant.

How Does It Work? Job descriptions, intake notes, and job postings can be shared with the Hiring Assistant, that will translate the information into role qualifications and build a pipeline of qualified candidates. The AI agent will also identify past applicants in their Applicant Tracking System via Recruiter System Connect. 

The human presence remains through hirers, who will be in the loop and can provide feedback to the candidates throughout the entire process. This way, the Hiring Assistant can be trained to continuously learn each recruiter’s preferences and offer more personalised support.

Further, AI paired with platform insights will aid skills-based hiring by providing candidate recommendations based on actual expertise, instead of traditional indicators like educational background or past employers.

AI in Recruitment 

Consulting firm Gartner has predicted that 80% of recruitment technology vendors will have AI capabilities embedded in their offerings by 2027.

At this point, the advantages of using AI to recruit are too substantial to ignore. Recruitment use cases for AI, such as chatbots, candidate matching, and career site optimisation will make the hiring process highly convenient for organisations and increase their business value.

Earlier, HR tech company GetWork, during an interview with AIM, revealed their platform GetWork.ai has been a game-changer for recruiters. To effectively track thousands of job applications landing on their platform, they have developed a feature that highlights a candidate’s percentile match relative to their resume and the job description.

Once the score is determined by the company or the minimum percentile match criteria is met, an AI voice bot instantly conducts interviews at a stunning rate of 1,000 calls per minute. This allows recruiters to hire potential candidates in just one day.

In a noteworthy development, a US-based startup Apriora AI has leveraged AI to streamline their hiring process. Their flagship product, AI interviewer Alex, represents a paradigm shift in recruitment methodology by seamlessly integrating advanced technology into the interview process.

Unlike traditional interviewers, Alex operates as a two-way AI interface, capable of conducting live video interviews with job candidates. This technology provides applicants with immediate feedback and a more transparent hiring experience.

A feature of Alex is its unparalleled capacity to manage interviews. Unlike humans, Alex does not require breaks or downtime, enabling it to conduct interviews continuously without interruption. 

How Do the Candidates Feel?

We had earlier discussed AI’s potential in the hiring process. It has been observed that AI is most commonly picked as candidates’ top choice because of its unbiased approach. 

According to Capterra’s Job Seeker AI Survey, 62% of job seekers believe they have a better chance of being hired if AI is used in recruiting and hiring processes, and 70% feel AI is generally less biased compared to humans when evaluating candidates. 

Recently, Chipotle introduced an AI member “Ava Cado” that will make the hiring process simpler, faster, and more automated for all its restaurants in North America and Europe. The restaurant company aims to cut hiring time by 75%. 

The system is trained to collect job applications, answer candidates’ questions, set up meetings, and send offers, all without human intervention.

What’s Next?

During the Oracle CloudWorld 2024 event in Las Vegas, Nagaraj Nadendla, senior vice president of Oracle Cloud HCM Product Development, told AIM that AI holds immense potential in streamlining HR tasks like recruiting, which he believes may eventually be carried out by a digital avatar. 

Speaking exclusively with AIM, Dominic Pereira, vice president of product management at Automation Anywhere, said that HR is one sector where automation can be adopted effectively and that he sees it happening at the earliest.

It should be noted that some existing Indian generative AI platforms including MachineHack for Enterprises, Oracle Recruiting, and Zoho Recruit have already been working to support recruiters and HR professionals in the hiring process.

The post 62% of Job Seekers Believe They Stand a Better Chance if AI is Hiring appeared first on Analytics India Magazine.

“Programming is no longer hard” or “everyone’s a developer” are the most common phrases that one would hear on LinkedIn or X as everyone is basically talking about Cursor, Claude, or GitHub Copilot. But the problem is that most of the people who claim so are not developers themselves. They are merely ‘modern developers.’

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Santiago Valdarrama, founder of Tideily and an ML teacher, who has been actively asking developers if they are using Cursor or not, started another discussion that tools such as Cursor and others are basically tools that can only assist existing developers in writing better code. “Wake me up when companies start hiring these clueless modern ‘developers’,” he added.

He gave an analogy of calling yourself an F1 racer after playing a racing game on an iPad.

In all honesty, it is undeniable that the barrier to entry for becoming a developer has dropped significantly ever since Cursor, even ChatGPT, dropped. People have been able to build software for their personal use and even build apps in mere hours. But, this does not eliminate the fact that it is currently only limited to just creating such apps and low level software.

“You Can’t Correct Code if You Don’t Know How to Code”

Given all this hype around the end of software engineering roles, developers and programmers are getting worried about the future of their jobs. It is indeed true that software engineers have to upskill faster than anyone else, but the fear of getting replaced can be pushed off to at least a few years.

Having tools such as Cursor and Claude are only good enough if a developer actually knows how the code actually works. The real game-changer is how developers who use AI will outpace those who don’t. “The right tools can turn a good developer into a great one. It’s not about replacing talent; it’s about enhancing it,” said Eswar Bhageerath, SWE at Microsoft. 

AI only takes care of the easy part for a developer – writing code. The real skill that a developer has is reasoning and problem solving, apart from fixing the bugs in the code itself, which cannot be replaced by any AI tool, at least anytime soon. Cursor can only speed up the process and write the code but correcting the code is something that only developers can do.

Moreover, bugs generated within code with AI tools are not easily traceable by developers without using any other AI bug detection tool. Andrej Karpathy, who has been actively supporting Cursor AI over GitHub Copilot, also shared a similar thing while working. “it’s slightly too convenient to just have it do things and move on when it seems to work.” This has also led to the introduction of a few bugs when he is coding too fast and tapping through big chunks of code.

These bugs cannot be fixed by modern ‘developers’ who were famously also called ‘prompt engineers’. To put it simply, someone has to code the code for no-code software.

Speaking of prompt engineers, the future will include a lot of AI agents that would be able to write code themselves. The future jobs of software engineers would be managing a team of these AI coding agents, which is not possible for developers who just got into the field by just learning to build apps on Cursor or Claude. It is possible that the size of the teams might decrease soon as there would be no need for low level developers.

Upskilling is the Need of the Hour

That is why existing developers should focus on developing engineering skills, and not just coding skills. Eric Gregori, adjunct professor at Southern New Hampshire University, said that this is why he has been teaching his students to focus more on engineering than just programming. “AI is too powerful of a tool to ignore,” he said, while adding that existing limitations of coding platforms have been removed completely. 

“Hopefully, AI will allow software engineers to spend more time engineering and less time programming.” It is time to bring back the old way of learning how to code as modern developers would be tempted to just copy and paste code from AI tools, and not do the real thinking. 

The F1 driver analogy fits perfectly here. Most people can learn how to drive, but would never be able to become a race driver. The same is the case with the coding tools. But if all people need is prototyping and designing an initial code, AI driven developers would be able to do a decent enough job.

That is why a lot of pioneers of the AI field such as Karpathy, Yann LeCun, Francois Chollet, even Sam Altman, say that there would be 10 million coding jobs in the future, the ones that would require the skills of Python, C++, and others. As everyone in some way would be a ‘modern developer’, and most of the coding would be done by AI agents. 

It is possible that most of the coding in the future would be in English, but most of it would be about debugging and managing the code generated by AI, which is not possible for someone who does not know coding from scratch.

The post Wake Me Up When Companies Start Hiring Clueless Modern ‘Developers’ appeared first on Analytics India Magazine.

Connecticut-based Samsung subsidiary, HARMAN, is a global leader in audio, automotive, and connected technologies, offering a range of innovative and high-quality products and solutions to customers around the world. Founded in 1980 by Sidney Harman and Bernard Kardon, the company has grown to become a prominent player in the industry, with a strong reputation for delivering exceptional sound experiences, intelligent automation solutions, and seamless connectivity across devices and platforms.

Read more: Data Science Hiring Process at NoBroker

Harman’s portfolio includes a wide range of products and services, including audio and video systems, car audio, connected car solutions, professional audio and lighting equipment, and more. Harman is the parent company to a variety of brands like JBL, Harman Kardon, AKG, Mark Levinson, and Infinity Systems.

HARMAN empowers global enterprises with cutting-edge solutions that harness the immense potential of cloud computing, applied AI, data, IoT, advanced analytics, metaverse, quantum computing, cybersecurity, and 5G. The team uses data science to address a wide range of business issues in sectors like healthcare, communications, industry, retail, software, and hospitality.

Analytics India Magazine got in touch with Dr Jai Ganesh, chief product officer of HARMAN to know how the company implements AI in their daily lives and also their hiring process for talent and work culture. He is an alumnus of IIM-Bangalore and the University of Oxford. “We believe technology has the power to transform the world for the better and we build solutions that address some of the most pressing challenges facing enterprises and society,” said Ganesh.

Inside HARMAN’s Data Science Team

The AI, data and analytics team at HARMAN is 2500-strong. There is a centralised data science team as part of the chief product officer’s organisation, which is responsible for building AI-ML accelerators such as MLOps framework, building AI-ML based features and functionalities in products as well as creating proof of value demos. Each of their six key verticals consisting of healthcare, communications, industrial, retail, software, and hospitality has its own data science teams who work on client-facing projects.  

“HARMAN’s data science team has made significant contributions by incorporating machine learning and deep learning models into a range of applications, such as predictive analytics, computer vision, NLP, and graph analytics,” said Ganesh. These cutting-edge models enable HARMAN to enhance the customer experience and engagement by providing AI/ML-driven insights gleaned from various data sources. 

The data science team of HARMAN leverages both open source as well as commercial tools, applications and frameworks such as Python, TensorFlow, PyTorch, AWS, Azure, or Google Cloud Platform, Java, C++, Git, Jenkins, Docker, Kubernetes, R, Jupyter, SAS, MongoDB, Spark, Kafka, MySQL, RStudio,  KNIME, RapidMiner, H2O etc.

These models have diverse applications, ranging from predicting hospital readmissions with an accuracy rate of 93%, using over 50 inpatient data variables to identify risk factors, to powering conversational AI, recommendation engines, optimisation, and fraud-risk models. 

HARMAN has further cemented its position as an industry leader by unveiling its ‘Intelligent Healthcare Platform’ at CES 2023, which harnesses the power of AI and machine learning to provide actionable insights that improve customer engagement through predictive analytics. When it comes to customisation, HARMAN has also implemented AR and VR on JBL’s customisation page. 

Hiring Process

From freshers to senior positions, the company hires for different laterals. 

The interview process for hiring data science roles includes at least five rounds where the candidates are assessed on their conceptual, technical, problem-solving, team-playing, coding, and learning strength.

One of the most common mistakes candidates make is they don’t research well about the company before applying and focus only on technical skills.    

Expectations

HARMAN expects potential employees to have a strong foundation in programming languages such as Python, R, or Java. They should also be proficient in coding, debugging, and testing. It is critical to have a solid understanding of linear algebra, calculus, probability theory, and statistics to comprehend the underlying concepts of machine learning algorithms. 

Familiarity with ML algorithms such as supervised learning, unsupervised learning, reinforcement learning, and deep learning is essential. Experience in data cleaning, transformation, feature engineering, and normalisation is also important to prepare data for machine learning algorithms. Additionally, good communication skills, problem-solving skills, and a willingness to learn new concepts, algorithms, and technologies are required to excel in this constantly evolving field.

Read more:  Data Science Hiring Process at Park+

At the same time, employees can expect to have the freedom to think innovatively and work with a team of ambitious individuals from around the world and actively grasp the opportunities for learning, growth, and personal development. 

Work Culture

“HARMAN’s people are the biggest distinguishing factor that set the company apart from its competitors,” said Ganesh. 

HARMAN’s culture prioritises support, innovation, and excitement, and their diversity fosters innovative thinking. The company makes sure that you balance your personal and professional life well. Employees collaborate from different backgrounds to find innovative solutions and achieve technical successes. It offers employees a place to grow and feel like family.

Besides flexible working hours, hybrid office, and health insurance, HARMAN has other special perks for its employees, including the ReInventHers initiative, which focuses on aiding women who are resuming their careers after a break, and the AMIGO Maternity Engagement Program, which provides support to women employees during and after pregnancy. 

So, people who are comfortable with numbers who aim to make it big, maybe the right fit for HARMAN. 

Click here to apply. 

The post Data Science Hiring and Interview Process at HARMAN appeared first on Analytics India Magazine.

Since its debut in 2011, Mumbai-based Pepperfry has been a game-changer in the way Indians shop for furniture and stylish home decor. Pepperfry was one of the early adopters of cutting-edge technology when it launched its online marketplace. Apart from classy furniture, Pepperfry is known for its wide range of furnishings, lighting, and other home utility products. Started by former eBay executives Ambareesh Murty and Ashish Shah, Pepperfry caters to every taste and requirement for every type of home.

AIM got in touch with Devvrat Arya, vice president of technology, at Pepperfry to understand how the furniture giant is implementing data science and who are cut out for such roles.

“We are at the cusp of a new era where AI will become ubiquitous, and machine learning and deep learning will power everything. To succeed in this field, one must have a strong foundation in linear algebra and statistical analysis, as every algorithm is based on these fundamental concepts,” said Arya.

Pepperfry’s AI & Analytics Play

Pepperfry’s data science team is fairly small and focuses on addressing problems related to the company’s customers and business. The organisation strongly believes in the super-lean methodology and begins by defining problem statements before seeking individuals to build a team around the project. This approach also governs the team’s structure and hiring process, which align with the super-lean methodology.

Pepperfry implements AI/ML models to improve the customer experience and one specific area that they focus on is anomaly detection. The data science team is analysing historical order-placed patterns from the past year and examining all possible combinations. By using various ML algorithms, the team can identify order anomalies that deviate from the predicted order-placed pattern logic. Once an anomaly is detected, the ML engine automatically notifies the relevant parties of the possible root cause of the issue. The data science team and developers collaborate to address the problem and prevent any technology leaks that could negatively impact business outcomes. 

Although in the beta stage, Pepperfry has also developed a visual search engine that allows customers to upload an image of furniture they’re interested in and receive recommendations for similar products. The algorithm compares the uploaded image with database images to find the top similar products. This feature simplifies the process of searching for furniture and decor items and provides insight into customer preferences for a more personalised shopping experience.

Read more: Data Science Hiring Process at Pepperfry

Interview Process

Pepperfry’s interview process for hiring data scientists involves several stages. First, the candidates are given an initial assessment consisting of complex and randomised data science and Python-based questions to assess their skills and knowledge. The first interview round evaluates the candidate’s analytical skills and suitability for the role based on personality traits. In the second technical interview round, the focus is on testing the candidate’s knowledge and understanding of multiple machine learning and deep learning algorithms. Lastly, there is an HR round for negotiating the salary and the issuance of an offer letter.

Skill Set Required

Pepperfry emphasises the importance of a candidate’s personality fitting the role they are applying for. The company’s initial evaluation of potential candidates focuses on several personality traits, including analytical skills, agility, communication abilities, honesty, curiosity, and strong work ethics.

The data science team focuses on visual-based customer experiences and has prioritised the use of deep learning-based libraries and frameworks. Tensorflow, Pytorch, and Scikit-learn are the primary tools utilised for data analysis and deep learning projects. Additionally, Fast RCNN and Yolo libraries are extensively used for object detection and segmentation, and SWIN transformers are employed for efficient image-based result classification. The company intends to delve deeper into GPT to increase organic traffic gradually.

Applicants with a strong background in linear algebra, probability theory, statistical analysis, and distribution will be viewed as an asset.

Arya, who has interviewed over 500 data science engineers, has noticed that the most common mistake candidates make is by attempting to apply a simplistic statistical and analytical approach to the ML process. This, he says, is inadequate as the disparity between statistical and ML analysis is significant, and a uniform strategy is unlikely to be effective in solving the problems at hand. 

Work Culture

“Pepperfry, as an organisation, values a culture that is diverse and inclusive and promotes positivity,” said Arya. Here, about 35% of employees are females against 65% males. Despite being in existence for over eleven years, they still maintain a start-up mentality through continuous innovation while remaining focused on its business goals. The company employs a flat organisational structure, which eliminates communication barriers between colleagues. 

“We have abolished the cabin culture this year, and now all employees sit together regardless of their role for faster and easier ideation, discussions, and conflict resolution within the organisation,” he added.

Pepperfry offers various perks to its employees, such as a hybrid working model, the flexibility to choose working hours, and ensuring a healthy work-life balance. The company consistently invests in its employees to enhance their skills and expertise in the latest technology and tools and provides access to top-notch learning resources and certification programs.

“If you have the knack for solving complex and innovative problems with the right mix of startup energy, Pepperfry is the place for you,” concluded Arya.

Click here to apply. 

Read more: Data Science Hiring Process at Livspace

The post Data Science Hiring and Interview Process at Pepperfry appeared first on Analytics India Magazine.

Philips Innovation Campus (PIC), the research and development arm of Philips in India, driving innovation across healthcare, lighting, and consumer lifestyle products, since its establishment in 1996. With a talented team of engineers, scientists, and researchers, PIC develops cutting-edge technologies, products, and solutions that meet the demands of both local and international markets.

PIC uses AI to create solutions that integrate into the workflows of healthcare providers and daily health routines of people. These AI-enabled solutions aim to augment the expertise of healthcare providers and support their decision-making, improve operational efficiency to help healthcare providers focus on patient care and empower people to take better care of their health and well-being.

Inside Philips AI & Analytics Play

“At Philips, we believe the value of AI is only as strong as the human experience it supports. That’s why we combine the power of AI with deep clinical knowledge to create solutions that integrate into the workflows of healthcare providers and the daily health routines of people”, said Ajit Ashok Shenvi, Head of Data & AI, Philips Innovation Campus, told AIM.

The data science team at Philips has recently developed several innovative health tech solutions using AI and machine learning technologies. These include the Philips Sonicare Prestige 9900, an electric toothbrush that uses sensors and a mobile app to help users improve their brushing technique, the Philips Radiology Smart Assistant, an AI-based solution that provides feedback to radiographers to improve their patient positioning skills, the EPIQ CVx and CVxi, cardiac ultrasound solutions that use AI-based models to provide semi-automated measurements for cardiac anatomical and functional quantification, and eCareManager, a telehealth program that uses advanced clinical algorithms to synthesize large amounts of patient data and identify at-risk patients for resource allocation.

For each business vertical, Philips has identified clear and focused themes in terms of AI capabilities to be integrated into our propositions. They are as follows-

• Personal Health (PH)- For PH, AI propositions centre around customer experience, engagement, healthy lifestyles and risk management.
• Precision Diagnosis- AI features throw light on diagnostic and workflow improvement, managing patient flow and health outcomes.
• Image Guided Therapy- Here, AI uses cases focus on workflow improvement, health outcomes as well as clinical intelligence and customer experience.
• Connected Care- For this specifically, AI is deployed for medical monitoring, clinical operations management, engagement, coaching, and early detection.

The businesses of Philips cover various aspects of the health spectrum, ranging from personal health to image-guided therapy, and offer diverse solutions. Therefore, the company avoids a one-size-fits-all approach. 

While the company uses a common cloud platform infrastructure with some rules, there is flexibility for customisations based on the customer’s needs and the nature of solutions. 

The base cloud platform is built on Amazon Web Services (AWS) and uses tools and frameworks from Microsoft, Google, and other providers. Moreover, the company has developed in-house tools and frameworks for specific purposes such as annotation, responsible AI, and data governance.

Additionally, transformer models are being used for multi-lingual clinical reporting applications. GPT and other large language models are being used for automated reporting analysis and preparation in our team. AR and VR are also implemented on demand and employed as and when relevant basis the customer need and use cases.

For example, Web-AR experience for Philips e-commerce products, and the advanced room planning for image-guided therapy use the mentioned emerging technology.

Read more: Data Science Hiring Process at Pepperfry

Interview Process

The hiring process for data science candidates typically involves a few standard tests and hackathon problems to demonstrate basic competency, followed by three to five rounds of technical and aptitude tests. Candidates who pass these stages will then be interviewed by the team manager and HR manager, with evaluation criteria varying depending on the specific data science team.

The key result areas (KRAs) used to assess data science candidates include their proficiency in machine learning (ML) and deep learning (DL), expertise in building AI solutions, familiarity with cloud-based AI platforms like AWS SageMaker, knowledge of the healthcare domain, understanding of regulatory guidelines and privacy guidelines, expertise in data management and governance processes for healthcare data like sensitivity to personal data, consent, access, awareness of data and AI bias, and deep understanding of underlying data sets.

Dos and Don’ts

According to Shenvi, the common mistake among candidates is focusing solely on building AI models in data science. However, the field encompasses end-to-end data and AI solutions that address customer needs, including accessing quality datasets and deploying solutions. 

“It is not only about technology push but most importantly about addressing a customers’ end-to-end needs” he added. 

Shenvi emphasised the importance of candidates recognising and comprehending the difference between AI models in the general AI field and those in the regulated sector, such as healthcare. In the latter, the process of constructing and implementing AI solutions is highly rigorous. Therefore, candidates should appreciate and understand these distinctions.

Expectations

“We seek to hire candidates for a long-term association, who possess solid theoretical background and proven expertise in building AI/ML solutions. They must be inquisitive and willing to learn both state-of-the-art and apply them to solve end-user problems,” Shenvi added. 

Philips prefers candidates with a T-shaped profile and experience in healthcare. The right mindset is the most important criterion for selection.  

On the hand, when it comes to the candidates, Shenvi says that they can “expect to solve unique challenges and problems in the healthcare space and improve the end user life” 

Successful candidates will have the opportunity to work with one of the top AI/ML experts in the health tech industry and gain a deep understanding of various clinical modalities. In addition to this, they can look forward to a workplace culture that emphasises work-life balance and a commitment to continuous learning throughout their career.

Internship

If you are in college and looking for internships, Philips has got you covered. 

Philips follows a centralised process to recruit interns from select educational institutions. The process involves candidates applying for roles, followed by interviews and mentorship at Philips. 

To be eligible for the internship, students must possess strong academic credentials and relevant project experience in their field. Additionally, holding certifications in reputable programming and data science courses would be advantageous.

Work Culture

“Our work culture is guided by core values such as customer focus, patient safety, quality, teamwork, ownership, and eagerness to improve. We believe that every individual can make a difference in driving the best outcomes for our customers, patients, consumers, and colleagues” Vishpala Reddy, Head of Human Resources, Philips Indian Subcontinent told AIM.

Philips’ gender distribution throughout the company is 39% on average. Around 1,300 women are in senior management positions, such as senior directors and executives. 

To create a more inclusive workplace, Philips prioritises transparency and gender balance in senior leadership positions, provides unconscious bias awareness training, and fosters diverse talent through employee resource groups and mentoring. Additionally, we aim to combat the digital divide by ensuring accessibility to technology and providing training opportunities for all employees. Leaders should continually invest in resources and training to upskill employees and stay up-to-date with market trends.

“Our inclusive culture prioritizes oneness, and we do not disclose employee representation based on sexual orientation, gender identity, expression, or disability,” concluded Reddy.

Read more: Raining Quantum Investments, But Talent Still an Issue

The post Data Science Hiring and Interview Process at Philips Innovation Campus appeared first on Analytics India Magazine.

When it comes to healthcare, global management consulting and technology firm ZS is a leading player. Headquartered in Illinois, the company has worked with top 50 pharmaceutical companies, and catered to 21 out of 25 top medical technology firms.

ZS covers the entire value chain of AI and ML, starting from research and extending to value realisation. It has an AI research lab dedicated to developing innovative and cutting-edge AI concepts. Their team of experts focuses on creating and deploying tech solutions that enable clients to expand their influence across different units, brands, and regions.

It has made an array of acquisitions in the health sector with the recent most being Trials.ai, a healthcare-focused, pure-play AI and analytics company. ZS also acquired analytics company Intomics that works with biomedical big data to infer biological insights and solutions providing digital health firm Medullan. Some of the primary clients include Pfizer, Eli Lilly and Company, Johnson & Johnson, Merck and so on. 

“We are constantly experimenting with generative AI and AR/VR to create immersive experiences for our customers,” said Manish Menon, office managing principal, ZS, in an exclusive interaction with AIM.

ZS recruits associates, associate consultants and consultants.

Inside ZS’ AI & Analytics Team

With over 3,600 employees on the AI and analytics team, the team leverages cutting-edge tech to work on both client-billable projects and internal assets. The data science team is composed of professionals with different levels of expertise. There are currently 125 associates, 56 associate consultants, 31 consultants, 19 managers, and four associate principals in the team.

The company provides advanced AI-based data management solutions, specialising in semantic graph technology. Their smart algorithms optimise data services and enable in-house capabilities. They offer services like data lake development, warehouse migration, training, and organisational redesign. They also provide a customised solution integrating primary, secondary, and social data through their AI-powered cloud-native platform, ZAIDYN.

The team uses ML, statistical analysis and computing, deep learning, data visualisation, NLP, problem-solving skills, model deployment, data structures and algorithms, and neural networks in their daily work. 

Additionally, the team also uses the following tools, applications and frameworks: Power BI, Microsoft Excel, Tableau, Python or R, image recognition, fraud detection, speech recognition, augmented reality, random forest, NumPy, XGBoost, Pandas and more.

Interview Process

“We look for candidates who understand data science, AI, ML, NLP, BERT, NER, supply chain, operational research, clinical trials, pharmaceuticals, healthcare, and have expertise in Python and R,” said Menon.

For junior-level positions, there are three interview stages that include coding challenges, aptitude questions, and ML case studies, panel presentation, 60-minute discussion of a real-world business problem and more. 

On the other hand, consultants in senior positions have similar but more intrusive technical rounds where they discuss the candidate’s CV and assess their expertise in ML, advanced algorithms, and data science projects. In the unstructured case study round, candidates analyse a real-world business problem and develop a comprehensive framework for solving it. The evidence-based interview requires candidates to present a data science project, showcasing their approach, methodology, and valuable insights. 

Read more: Data Science Hiring Process at HARMAN

Skills Needed

When it comes to the understanding of tech tools, data science candidates should be familiar with Power BI, Microsoft Excel, Tableau, Spark, Python, R, SAS, NLTK, among  others. They should have sufficient knowledge in healthcare, image recognition, fraud detection, speech recognition, augmented reality, and a wide range of other domains. Moreover, candidates should also have a solid grasp of frameworks such as TensorFlow, Scikit-learn, Pandas, PyTorch, Matplotlib, NumPy, Seaborn, XGBoost, and random forest.

ZS uses several key performance areas (KPA) and key result areas (KRA) to assess the data science candidates. 

KRAs

• Formulating questions aligned with the objectives of the organisation
• Performing data inquiries and exploratory analyses to address those inquiries
• Consolidating and manipulating data from diverse origins
• Selecting suitable models and algorithms to steer the data analysis procedure
• Proficiency in coding languages such as Python and R

KPAs

• Net promoter score (NPS): This metric captures the level of customer satisfaction gathered through surveys and distributed among internal stakeholders.
• Dollars saved (or earned) through data products: Evaluates the value generated by the team for the company in terms of monetary savings or gains.
• Number of incidents per product monitored: Checks the dependability of data science products by measuring the frequency of incidents per product.
• Time to incident resolution: Examines the efficiency of data science teams in addressing and resolving incidents, ensuring that key products are restored promptly.
• Cloud computing costs per team member: Quantifies the expenditure on cloud computing resources per team member, offering insights into the efficient use of computational resources by data science team members.

Expectations

Menon emphasised on the fact that the candidate looking to pursue a career in data science should make it a standard practice to gather information from the hiring team about the company, the clients they will work with and the specific skill set they will be hired for.

It is important to have an appealing resume that showcases your previous projects and proficiency in different data science skills. Employers also prefer candidates who have a consistent work history and have good communication skills and practical problem-solving approach. “Candidates should be transparent and honest about their preferences and counteroffers to foster a better working relationship with the hiring team and provide a foundation for successful collaboration,” Menon explained.

Read more: Data Science Hiring Process at Pepperfry

Work Culture

ZS is a values-driven organisation that aims to foster collaboration and teamwork towards shared goals based on principles of impact, growth, and empowerment. 

ZS India boasts a 31% female representation in its workforce and has adopted a merit-based approach in hiring and promotions with clear policies around inclusivity, as well as a zero-tolerance policy for any violations of these values. “We aim to create a supportive and dynamic work environment that promotes personal and professional growth and cultivates a shared sense of purpose among our employees,” Menon added. 

In addition to providing employee rewards and benefits like health insurance, maternity leaves, food, and transportation allowances, ZS also provides the following perks.

• Paternity, adoption and family medical leaves
• Mental health support for employees and family members, financial planning services and legal counselling
• Wellness programs—from nutritional and physical to stress management
• Employee retention bonus with increasing amounts over four years

So, if you are looking to make an impact as a data scientist in an environment that helps you grow both professionally and personally, ZS is the right fit for you. 

The post Data Science Hiring and Interview Process at ZS appeared first on Analytics India Magazine.

Founded in 2015 by Vidit Aatrey and Sanjeev Barnwal, e-commerce platform Meesho has over 100 million customers. It recently surpassed a record 1.1-million seller mark on its platform, attracting over 600,000 small enterprises within the last 12 months. Backed by the likes of SoftBank, Meta, Y Combinator and Fidelity Investments, Meesho allows anyone to start their businesses with zero investment or inventory.

Over the years, the leading online marketplace platform has used AI and analytics to revolutionise the way we shop. Besides implementing existing AI and ML models, Meesho also has customised frameworks. By leveraging data science to personalise product recommendations, optimise prices, detect fraud, and provide customer support, Meesho is making it easier and more convenient for customers to find and buy the products they want.

Analytics India Magazine got in touch with Debdoot Mukherjee, chief data scientist, and head of AI and demand engineering at Meesho, to understand how the e-commerce giant is dabbling in AI and ML and what is their hiring process for data science candidates. 

Read more: Data Science Hiring Process at NoBroker

Meesho’s AI & Analytics Play

“At Meesho, AI and ML are fundamental components to solve a wide range of problems across every aspect of an e-commerce platform,” Mukherjee told AIM.

On the demand side, Meesho implements AI and ML to engage users through push notifications and provide personalised feeds. Recommendations play a crucial role, with a significant percentage of their orders coming through recommendation algorithms powered by ML and deep learning models.  The recommendations rely on a combination of user and product understanding, which involves analysing user interaction data and decoding product content using computer vision and NLP models. 

Meanwhile, the company empowers its suppliers by offering efficient product cataloguing. With just a simple photo, their automated cataloguing process, driven by computer vision models, streamlines the task. They also assist suppliers in optimising their pricing through guided suggestions powered by ML techniques.

“In terms of fulfilment, we enhance order efficiency by addressing customer issues, detecting and preventing fraud, and resolving any issues that may arise in the system. These functions heavily rely on large-scale ML models,” said Mukherjee.

The 60-strong data science team is also actively experimenting with generative AI in various applications like recommendation systems to rewrite or correct user queries and enhance cataloguing through richer visual representations and virtual try-on experiences.

The company uses TensorFlow or PyTorch for most of its model training. They also use a lot of tree-based models like XGBoost. Databricks is used as the data engineering platform, which includes a data lake and other infrastructure components such as the offline feature store. 

In addition to pre-existing models, Meesho also develops tailor-made tools based on specific requirements. This encompasses their exclusive model to detect shopping intent, built upon cutting-edge transformer models employing deep learning techniques. For cataloguing, Meesho has built custom models built on existing computer vision models to extract detailed information about products, starting from their images.

For fraud detection, Meesho has developed a proprietary system that effectively identifies and catches a significant number of fraudulent activities. It specifically addresses issues like Return to Origin (RTO) fraud, which is a major concern in the Indian e-commerce market. Meesho also has systems in place to counter other types of fraud. 

Read more: AI Cloud Wars: Azure AI vs Vertex AI

Interview Process

The selection process begins with rounds focused on coding skills and fundamentals. Candidates are tested on their ability to build systems, including ML systems, in applied environments. 

Subsequent rounds assess ML fundamentals, theoretical knowledge, and the use of core algorithms. Later, candidates face application-focused rounds with case studies, where they must develop ML solutions for real-world problems. The company values candidates’ coding skills, data science expertise, and problem-solving abilities in practical scenarios.

Candidates applying for the role are assessed on three components. Firstly, they need expertise in machine learning and AI, encompassing knowledge of algorithms, statistics, and ML properties. Secondly, a strong foundation in computer science is necessary to develop scalable systems and apply ML techniques on a large scale. Lastly, they must possess product thinking skills to identify problem formulations that improve business metrics and address customer issues.

Read more: Jeffrey Ullman’s Unsettling Ultimatum 

Work Culture

“The Meesho AI team consistently pushes the boundaries of the state-of-the-art and encourages the development of cutting-edge systems. I believe that candidates joining our team should anticipate a promising and exciting set of problem statements to solve,” added Mukherjee.

With a problem-first mindset, Meesho encourages curiosity, creativity, and finding new solutions. The ‘Meesho Mantras’ foster a people-centric workplace, promoting employee happiness and engagement. Meesho values teamwork and collaboration, rewards speed over perfection and encourages diverse perspectives. 

Employees are empowered to take ownership of their work, make decisions and take bigger risks. The organisation takes pride in providing top-notch employee benefits, including maintaining a healthy work-life balance, comprehensive health insurance and offering flexible leaves. 

“AI can touch users’ lives firsthand at scale across hundreds of millions of users. So if you care about creating impact at scale, Meesho is the right fit,” concluded Mukherjee. 

Read more: Data Science Hiring Process at ZS

The post Data Science Hiring and Interview Process at Meesho appeared first on Analytics India Magazine.

Building on 2022’s legacy characterised by layoffs, numerous tech giants have succumbed to the practice of downsizing even in this year. This can be seen as a strategic response to combating the macroeconomic conditions and addressing the repercussions of over-hiring. However, beating the odds, there are a few companies that are steadily hiring. Here, we have compiled an extensive catalogue of open positions at companies that are actively seeking out professionals.

Microsoft

Microsoft is looking to add a partner research director, IDC who will oversee team management, emphasise the cultivation of diverse and inclusive team dynamics, collaborate with leaders and the wider organisation through cross-functional collaboration, and establish long-term growth plans in collaboration with geographically dispersed research teams. Additionally, they will devise research strategies and effectively supervise skilled individuals specialising in data science, machine learning, and threat hunting. The employee will be responsible for developing key metrics, forecasts, and reports, partnering with peer teams across geographically diverse research divisions.

Minimum Qualifications:

• Over 15 years of experience successfully managing result-oriented R&D teams that deliver solutions for customers.
• Experience in leading large teams and overseeing multiple levels of management.
• Exceptional communication skills across different groups and adept at issue management.
• Background in the protection/security industry.
• Proficiency in understanding attack patterns in identity, endpoint (Windows, Linux, Mac, and mobile), and cloud/SaaS systems.
• Familiarity with security incident response, forensic investigation, and threat intelligence.
• Experience speaking at security-focused conferences on threats and protective measures.

Location: Hyderabad, Telangana (Hybrid) 

Click here to apply. 

Read more: Data Science Hiring Process at Meesho

Cisco

Networking giant Cisco is in search of a skilled data scientist to add business insights to various customer experience entities. This requires a combination of extensive domain expertise and technological proficiency. The primary responsibilities include developing a deep understanding of the business domain and fostering collaboration among stakeholders. Additionally, the data scientist will assist in designing and developing solutions, implementing them effectively, and promoting data-driven decision-making. The ultimate goal is to drive significant business outcomes, such as revenue growth, cost reduction, and improved customer and employee satisfaction.

Minimum Qualifications:

• A master’s or bachelor’s degree in management, operations, statistics, mathematics, or computer science, along with a minimum of five years of professional experience.
• Strong background in various domains such as business consulting, statistical modelling, applied machine learning/AI and data-driven storyboarding.
• Candidate should possess proficient skills in SQL, R, and Python.

Location: Bangalore, Karnataka

Visit Cisco’s career page to learn more about it. 

Zoom

Online video communications platform Zoom has an open position for a data analyst to support important marketing, pricing, revenue, and compliance initiatives. The role involves identifying and measuring the impact on customer audiences on a weekly basis. The analyst will become an expert in revenue and subscription data for the online business, improving existing processes for increased productivity. They will also create dashboards to measure the outcomes of new growth initiatives and ensure the delivery of relevant insights to online partners. The analyst will conduct in-depth research and analysis to discover new business opportunities and use advanced analytics techniques such as incrementality analysis, forecasting, and modelling.

Minimum Qualifications:

• A bachelor’s degree and a minimum of five years of experience utilizing data for decision-making. 
• Strong SQL skills along with excellent communication abilities, problem-solving skills, and a growth mindset suitable for a dynamic business environment.

Location: Bangalore, Karnataka

Check out the opportunity here.

JP Morgan & Chase

JP Morgan & Chase, a financial services company, is actively involved in AI development. Their AI software service, IndexGPT, aims to provide financial assistance. Currently, they are seeking a vice president – annotation (lead), conversational AI product. This role involves data labelling and annotation for machine learning models. Responsibilities include sifting through data, identifying relevant content, and applying accurate labels. Tasks include comprehending business objectives, establishing entity relationships, validating model results, and providing feedback for improvement. The role also includes developing workflows, processes, and KPIs for measuring annotation performance and ensuring quality. Collaboration with cross-functional teams to design data annotation projects and leadership in data annotation operations are key responsibilities. 

Minimum Qualifications

• Minimum 10 years of work experience, with more than seven years in data collection, analysis, or research, and around three years leading small teams.
• Strong knowledge and experience in the financial domain.
• Ability to work independently and collaboratively to achieve project goals.
• Curiosity, diligence, and attention to detail, with a motivation for solving complex analytical problems and an interest in data analytics techniques.
• Interest in machine learning and the ability to develop a working level of domain knowledge in machine learning concepts.
• Understanding of model scoring parameters, including precision, recall, and f-score.

Location: Bangalore, Karnataka

Apply here. 

Read more: Believe it or Not, 55% of Digital Frauds Happen Via UPI

Accenture

Accenture is expanding its team with the addition of an AI platform engineer who develops applications and systems that use AI to improve performance and efficiency. The primary responsibilities include building predictive models, developing advanced algorithms for data analysis, evaluating emerging technologies, identifying new data patterns, deploying models in production, and integrating front-end and back-end systems. 

Minimum Qualifications

• Proficient coding skills and familiarity with Python, R programming languages. 
• Prior experience in data analysis and a strong interest in utilising data for statistical modeling and extracting valuable insights.
• Experience working with machine learning data mining toolkits such as NLP, Semantic Web, R, Core NLP, NLTK, as well as information retrieval libraries like Lucene/SOLR. 
• Ability to thrive in a fast-paced, test-driven, collaborative, and iterative programming environment.

Location: Hyderabad, Telangana

Click here to apply for this. 

The post Top 5 Companies Hiring for Data Science Roles appeared first on Analytics India Magazine.

Founded in 2015 by IIT graduates, Satish Kannan and Enbasekar Dinadayalane, MediBuddy, a prominent player in India’s digital healthcare sector, offers online and offline consultations, doctor appointments, clinics, prescription delivery, home lab tests, and more.

With over 2,000 employees, MediBuddy covers primary and tertiary care, including surgeries and second opinions, and offers preventive healthcare services. The portal boasts an alliance grid comprising over 90,000 physicians, more than 7,100 medical facilities and clinics, over 4,000 testing and examination hubs, and over 2,500 pharmaceutical outlets.

MediBuddy is backed by the likes of India Life Sciences Fund III, Quadria Capital, InvAscent and Lightrock India, among others. In February 2023, MediBuddy acquired ‘vHealth by Aetna’ to enhance its market dominance and strengthen its overall presence. 

The health-tech company also expanded in rural India by acquiring Clinix, a healthcare provider with an extensive network across 20 tier-two and tier-four cities. This acquisition enabled MediBuddy to reach and serve the healthcare needs of people in remote areas of India.

MediBuddy is currently hiring data scientists (level 2 and level 3). 

Inside MediBuddy’s Data Science Team

“We have a diverse tech team consisting of over 200 people, with a unique multidisciplinary data team that includes various professionals such as doctors, nurses, and clinicians besides data scientists, data engineers, analysts, product managers, lab pathologists, lab technicians, and clinical experts,” Dinadayalane told AIM in an exclusive interview. 

The team also includes data annotators, who are nurses and clinical staff responsible for tagging and cleaning the data. “While we have doctors who can code, we don’t have engineers who have transitioned to become doctors,” he added jokingly. 

Over the years, MediBuddy addressed various problems using AI and analytics. In 2015, the digital healthcare startup launched its app, placing digital healthcare at the forefront. Initially, they focused on online consultations, which became essential during the COVID-19 pandemic. 

One of the primary impacts of the data science team can be seen in Clara, MediBuddy’s proprietary data science system developed by the company. It includes a chatbot for streamlined patient-doctor interactions, a clinical decision support system for doctors, quality control measures for improved patient experience, and an AI-based database engine for prescription accuracy. Clara also assists in providing additional healthcare services like medicine delivery, lab tests, and surgeries.

Another problem that the company is solving through AI is handwriting recognition from prescriptions which often turns out to be a deciding factor in getting the right medication. They are exploring technology solutions like AWS TextTrack and OCR-based technologies to automate interpretation for this. Another priority for MediBuddy is quality control, with robust data-based analytics and quality control engines. These engines analysed audio, video, and text data to assess interactions between doctors and patients. Critical insights were flagged and forwarded to internal clinical experts for improvement.

Taking into account India’s rich linguistic diversity, with more than 350 languages spoken, MediBuddy has undertaken a thoughtful approach to cater to the larger population who may not be proficient in English. They have implemented word embeddings to effectively tackle the unique challenges encountered in Indian English, such as the blending of Hindi words within English text.

Moreover, the MediBuddy team has dedicated efforts towards constructing models that are capable of language classification, named entity recognition, and establishing meaningful connections across various languages, leveraging extensive datasets. They recognise the significance of language teams in accurately tagging and comprehending subtle nuances within the linguistic landscape.

The company also created a doctor assistant engine, integrating AI-based auto-suggestions and recommendations. Based on chat conversations, the system suggests potential diagnoses, additional questions, and appropriate medications for doctors to review and validate. However, no decision is made without a doctor. It is just for suggestions to empower doctors to make well-informed decisions and prevent prescription errors.

To address this set of unique challenges, MediBuddy also has a diverse range of tools and frameworks. 

On the data science side, Python is primarily used as the scripting language. The team employs various NLP models as their work involves chat, call, and video consultations. Speech processing, audio and video processing, and text processing are key components, as chatbots are built using NLP engines. Additionally, the team works on video quality and conversation smoothness. In terms of data modeling, the team explores emerging models like GPT-2 and GPT-3 and SVM (support vector machines) for NLP.

MediBuddy uses Superset, an open-source analytics platform, and R for data analytics tasks. Airflow is employed for data engineering and managing data pipelines. Cloud platforms such as AWS, Google, and Microsoft are used to address specific AI and healthcare requirements, including the use of TensorFlow. 

Read more: Data Science Hiring Process at Meesho

Interview Process

Candidates have introductory conversations to assess their fit for the role. This is followed by two or three rounds of technical interviews, consisting of in-person or take-home assignments depending on the role and problem being addressed. For higher-level positions, there may be interviews with managers to evaluate leadership potential.

“Our data science work is not limited to a separate R&D team but is integrated into everyday work across various healthcare services,” said Enbasekar. 

When it comes to the skills that the company looks for in data science candidates, there are two core skill sets. Firstly, a strong understanding of fundamental modeling concepts is important. This includes expertise in mathematics, algorithms, and data science fundamentals. Secondly, candidates should have the ability to work with multiple functional teams and have a customer-oriented approach. Soft skills such as effective communication, teamwork, and customer understanding are highly valued.

From candidates, the company expects a willingness to learn and adapt to new technologies and challenges, as the field of data science is constantly evolving. MediBuddy values individuals who can bring solutions to life by collaborating with multiple teams and taking full ownership of projects. 

Work Culture

Enbasekar describes his company’s work culture as being focused on healthcare, with a strong sense of pride and humility about the impact they can make in the field.

“We have a high-performance environment with continuous learning and problem-solving, driven by the desire to improve and serve our customers, doctors, and hospital partners”, he opined.

End-to-end ownership is a key cultural value, where employees go beyond their core roles to ensure value for the end user. MediBuddy also encourages open interaction and collaboration across teams, promoting a non-hierarchical structure where individuals freely seek help and get things done. 

Why Join MediBuddy?

Joining Medibuddy offers the opportunity to have a significant impact on healthcare on a large scale. “We address unique technological challenges in healthcare, providing a valuable learning experience. Operating in both B2C and B2B sectors, MediBuddy offers exposure to various aspects of the healthcare industry,” he concluded. 

So if you want to contribute to solving important challenges and ultimately benefit millions of people, making a meaningful difference in healthcare, MediBuddy is the right place for you. Check out their careers page here. 

Read more: Data Science Hiring Process at ZS

The post Data Science Hiring and Interview Process at MediBuddy appeared first on Analytics India Magazine.

The need for talented data engineers who can design, construct, and manage the systems that enable efficient data analysis has increased rapidly as organisations continue to acquire and store enormous volumes of data. These are the people who extract useful insights and make informed decisions based on data-driven analysis by efficiently managing and optimising data infrastructure.

Data engineering, as a field, is expanding at a rapid rate and is a pretty lucrative one at the moment due to the rising demand for data-driven insights and ever-growing technology. Let’s take a look at 10 companies that are currently hiring data engineers. 

Microsoft

Microsoft’s data and insights team is looking for a Data Engineer who is customer-obsessed with a growth mindset. The role demands managing data architecture, ensuring data quality and compliance, and working with other teams at Microsoft to improve customer opportunities and revenue growth. Applicants should have a bachelor’s or master’s degree in a related field, and at least two years of industry experience. Experience with big data technologies, data analytics tools, and data visualisation is preferred. The position is available in Bangalore or remotely.

Apply here. 

Read more: Data Science Hiring Process at Livspace

Google

Google is hiring for the role of Data Scientist (business and marketing). The ideal candidate must have a bachelor’s degree in computer science or equivalent subject, more than eight years of experience in data science and web technologies like HTML, CSS, JavaScript, and HTTP, and experience in customer-centric roles. As a member of gTech’s product and tools operations team, the successful candidate will be responsible for analysing data, building models, collaborating with cross-functional teams, and communicating insights to stakeholders. The position is open in Gurgaon, Hyderabad, or Bangalore.

Learn more about the job here. 

Apple

Apple is hiring a Search Software Engineer for their applied machine learning search team. The position will need the candidate to design and develop large-scale, distributed, and highly available systems and pipelines using Java tech stack. The ideal candidate should have over three years of experience working in Java and web services, in the search domain, and proven skills in designing scalable, highly available distributed systems that can handle high data volumes. The candidate should also have a deep understanding of information retrieval concepts and linguistic processing like tokenizers, speller, and stemmers. The position is open in Bangalore.

You can apply here. 

Read more: Can Apple Draw Better Maps than Google?

Pfizer

Research-based biopharmaceutical company Pfizer is seeking to add a Senior Associate Analytics Engineer to their data science industrialization team who can seamlessly build and automate high-quality data pipelines for advanced analytics, AI, ML, and other business applications. The ideal candidates should have a bachelor’s degree in analytics engineering or a related field with over two years of work experience, hands-on skills in analytics engineering, cloud-based analytics ecosystem, data ingestion, warehousing, and data model concepts, and proficiency in SQL and Python. The job location is flexible.

Airtel

We also have Wynk, Airtel‘s music streaming platform, looking for an experienced Data Engineer to join their data platform team in Gurgaon in a hybrid work setup. This mid-senior level position involves developing and maintaining a reliable, scalable, and efficient data pipeline. The ideal candidate should have at least five years of experience in Spark and Scala, along with excellent data structures and algorithm skills, and is comfortable working with Hadoop, Pig, Hive, Storm, and SQL. Besides this, they should be able to handle massive amounts of data daily and have a relevant degree in computer science or software engineering from a premier institute. 

Click here to apply.

IBM

Big Blue IBM is on the lookout for a Data Engineer who can develop, test, and support data solutions for customers across different industry verticals. They must also understand data architectures, platforms, governance, and information management. The ideal candidate should have at least three years of related experience, be skilled in modelling, cloud-based ETL services, TSQL code writing, and data warehouse schemas, among others. The candidate should be an excellent communicator and be able to manage competing priorities and delegate as and when required. The entry-level role requires a relevant bachelor’s degree. 

Apply here. 

Nike

Global apparel brand Nike is hiring a skilled Data Engineer who has programming and big data skills, and enjoys working in a fast-paced environment, for its enterprise data and analytics team. The Bangalore-based position needs designing and building simple reusable components, implementing product features, enhancing data quality, and supporting data management projects. The ideal candidate should have a bachelor’s degree in computer science, with more than two years of experience developing data and analytics solutions, and must be a quick learner. Furthermore, they should be familiar with workflow scheduling tools, source control tools, scripting languages, relational SQL, and building data lake solutions with AWS, EMR, S3, Hive, and Spark. 

Check out Nike’s career page for more details. 

JP Morgan Chase & Co

Financial giant JP Morgan Chase & Co is on a hunt for an SQL Developer to join its centralised data architecture team. The ideal candidate should be comfortable with data, have knowledge in building cloud-based applications, and experience building consolidated data models. They will be in charge of data analysis, data opportunity identification, development of consolidated data models, and cloud-based application creation. The candidate must possess technical proficiency in building dimensional data models, ELT/ETL procedures, experience with SQL and Python, and familiarity with MPP databases and big-data (Hadoop). 

Visit here to apply. 

Qualcomm

Qualcomm is hiring a Machine Learning Systems Engineer with the knowledge of deep learning frameworks such as Keras, TensorFlow, ONNX, PyTorch, and Caffe/Caffe2. This position needs someone with practical expertise creating CNNs and RNNs/LSTMs as well as other neural network architectures for CV, NLP, and NLG models. Excellent Python and C++ programming knowledge as well as knowledge of data structures and algorithms are important. Preferred skills would include experience of GPUs, machine learning accelerators, and related software. The applicant should hold a bachelor’s, master’s, or a PhD in engineering, machine learning/artificial intelligence, information systems, computer science, or a related discipline, as well as at least three years of experience in software engineering. The location for this position is Bangalore.

Apply here.

Deloitte

Deloitte is looking for a Data Engineer for their analytics & forensic technology practice and work on their new Anti-Fraud Waste and Abuse solution (AFES). AFES is responsible for detecting fraud, waste, and abuse in different industries using machine learning and state-of-the-art technology. The ideal candidate should have experience developing solutions with an agile development team, defining, producing, testing, reviewing, and debugging solutions, and creating component-based features and micro-frontends. Preferred qualifications comprise experience in Azure DevOps, data streaming, big data technologies, and business intelligence tools. The candidate should have a bachelor’s or master’s qualification. This position is based in Kolkata.

Click here to know more.

The post 10 Companies Hiring for Data Engineering Roles appeared first on Analytics India Magazine.

Online beauty and personal care brand Purplle became India’s 102nd unicorn after raising $33 million last year in Series E funding, led by South Korea’s Paramark Ventures. Founded by Manish Taneja and Rahul Dash, the Sequoia-backed company has also found investors in Premji Invest, Blume Ventures, and Kedaara Capital.

With a small but strong 11-member team, consisting of seven data scientists and four data analysts, Purplle stands out in its industry by harnessing the power of AI and analytics to refine the customer experience and drive operational excellence. The team employs data-based prognostic models to empower other business divisions in making concise and more impactful choices.

Data science is applied across various key functions and areas at Purplle. These include personalisation, product discovery, search and suggestions, recommendations, and data-driven supply chain management processes like box suggestions and DRR (delivery, return, and replacement).

Analytics India magazine got in touch with Vivek Parihar, Head of Engineering, Purplle, to understand how the e-commerce platform leverages data science in their daily operation and what they look for in potential candidates.

 Read more: This AI Startup From Paris Raises Highest Seed Funding Ever 

Inside Purplle’s AI & Analytics Team

“One of the main focuses of Purplle’s AI and analytics team is to provide personalised recommendations and optimising the search engine, resolving customer product discovery challenges through the use of AI and analytics,” said Parihar.

Purplle ensures that the appropriate products are displayed to the correct customers at the appropriate time, optimising their platforms by using personalised and customer-centric data. Moreover, AI is employed to tackle supply chain concerns, such as determining the most suitable source warehouse for efficient delivery.

One recent achievement of Purplle’s data science team is taking control of Purplle’s item views (IVs) planning process, transitioning it from a manual to a fully automated system. This allows for the prediction of product-level IVs, enabling the business teams to take informed actions and streamline their processes in collaboration with brands. By planning daily IVs, the algorithm effectively targets customers and optimises their experience.

Additionally, the introduction of a reinforcement learning-based algorithm, specifically the multi-armed bandit algorithm, has enabled Purplle to strike a balance between exploration and exploitation. This algorithm determines when to show the most performing products and when to showcase diverse products for exploration purposes.

Their tech stack is on Google’s cloud platform, including tools like Bigtable, BigQuery, Google Storage, Dataproc, Kubernetes, Redis, and Elastic Search, among others, to train and serve their data models. The team also uses Flagr and Open Source for AB testing, and Python as a programming language. From model building and deployment to serving, their entire pipeline is fully automated using CI/CD.

Interview Process

Purplle seeks individuals who possess robust analytical capabilities, a deep understanding of statistics, proficient programming skills, keen business acumen, and expertise in data visualisation techniques.

Purplle’s interview process for data science roles consists of two rounds for junior data scientists and three rounds for senior data scientists.

• Round 1: The first round is conducted with senior data scientists to assess a candidate’s technical proficiency and problem-solving abilities.

• Round 2: The second round takes place with the data science lead to delve deeper into the candidate’s domain knowledge and project experience.

For senior-level positions, an additional round takes place with the senior data product manager to focus on the candidate’s leadership skills and ability to contribute to the strategic vision of the team.

Finally, the “Level Up” round is included in the cultural fit assessment to evaluate candidates’ cultural fitment, ensuring alignment with the organization’s values and ability to thrive in the work environment.

Data science candidates are assessed based on key result areas (KRAs) or key performance areas (KPAs), which include evaluating the impact of their projects on business metrics, producing reusable artefacts, ensuring code and delivery quality, proactively resolving errors, optimising code, implementing new technologies or learning initiatives, maintaining consistent effort variance below 10%, managing bugs and issues, and achieving optimal performance in memory handling, CPU usage, and query optimisation.

Expectations

When joining Purplle’s data science team, new members are expected to value independence and excel in analysing and making decisions that positively impact the business and sales. Proficiency in coding, familiarity and strong graph with commonly used ML frameworks are essential technical skills. The team actively seeks individuals who can effectively apply their ML knowledge to evaluate, understand, and solve real-world business problems.

On the other hand, candidates can expect to work in a dynamic environment where they can make decisions autonomously, communicate freely, build and test hypotheses, and experiment. 

However, Vivek points out that some of the common mistakes that candidates make often include applying models and expecting them to solve business problems directly without intervention or analysis, and utilising complicated models without knowing the model mechanism and function clearly. 

Work Culture

At Purplle, the company maintains an employee-centric work culture with flexible policies, differentiating itself from competitors in the industry. The work environment fosters collaboration among teams, facilitated by frequent meetings with reporting managers and teammates, ensuring seamless communication throughout the organisation. 

The company boasts a flat organisational structure and a vibrant startup culture, actively cultivating cross-functional team connections and providing access to anyone within the organisation, regardless of their level or designation. 

When it comes to working with the data science team, Purplle stands out by fostering individual ownership and empowering employees. They use advanced data science tools and robust pipelines to capture user activity, providing hands-on experience with the latest technology. 

“The team is knowledgeable, resourceful, and ready to provide guidance. We promote cross-team learning and idea exchange. We encourage experimentation and analysis before going live, leading to valuable knowledge and rapid learning” he added. 

When it comes to perks, employees receive performance-based ESOPs after a stipulated period. The company also offers insurance, maternity leave, and other benefits like employee discounts on the Purplle shopping app.

Candidates applying to Purplle should remember to master the basics and focus on applying their knowledge to solve business problems instead of building fancy models to showcase their expertise,” he concluded. 

So if you have a penchant for analytical thinking, problem-solving, and a keen interest in working with data, you are ideally suited for Purplle.  

Read more: Data Science Hiring Process at Meesho

The post Data Science Hiring and Interview Process at Purplle appeared first on Analytics India Magazine.

German tech conglomerate SAP Labs has been one of the major players in the generative AI race on the enterprise side. The company recently introduced Joule, a natural-language generative AI assistant that allows access to the company’s extensive cloud enterprise suite across different apps and programs. It will provide real-time data insights and action recommendations.

With a global presence in 19 countries, labs are responsible for driving SAP’s product strategy, developing and localising its core solutions, and contributing to the SAP Business Technology Platform. 

54-year-old SAP was founded by five former IBM employees, Dietmar Hopp, Hasso Plattner, Claus Wellenreuther, Klaus Tschira, and Hans-Werner Hector. SAP Labs is the R&D arm of SAP with its second largest office space in Bengaluru. 

AIM got in touch with Shweta Mohanty, vice president and head, of human resources, SAP, India and Dharani Karthikeyan, vice president, head of engineering for analytics, SAP Labs India, to understand the company’s AI and analytics play, customer stories, hiring process for data scientists, work culture and more. 

AI & Analytics Play

“We have fully embraced generative AI in our business AI concept, aiming to provide AI that is responsible, reliable, and relevant. The goal is to infuse AI into business applications, with a focus on trust and outcomes,” Karthikeyan told AIM. 

SAP has a portfolio of over 350 applications spanning various use cases, from cash management to document scanning. The company is enhancing its Business Technology Platform (BTP) with a generative AI layer.  The team aims to improve business processes while maintaining human control over decisions. They have collaborated with Microsoft for Human Capital Management tools, combating biases in recruiting, and introduced a Business Analytics tool for faster insights. 

SAP is also partnering with Google Cloud to launch a holistic data cloud, addressing data access challenges. Additionally, they have invested in generative AI players Anthropic, Cohere, and Aleph Alpha, diversifying their capabilities.

Interview Process

The hiring process for tech roles involves five to six steps starting with profile screening, focusing on the candidate’s development background and programming language proficiency. As described by Mohanty, this is followed by an online assessment to test programming skills, lasting 60 to 90 minutes. Technical interviews include case studies to assess proficiency and hands-on experience. 

For senior roles, there’s a discussion with a senior leader to gauge cultural alignment. The final step is an HR discussion focusing on cultural fit and interest in the organisation. For college recruitment, the process includes live business solutions assessments. The process concludes with a rigorous background verification.

When it comes to finding the right fit for SAP labs, the ideal candidate should have a comprehensive understanding of ML algorithms, and to build and maintain scalable solutions in production,” added Karthikeyan, highlighting that this consists of the use of statistical modelling procedures, data modelling, and evaluation strategies to find patterns and predict unseen instances. 

The roles involve using computer science fundamentals such as data structures, algorithms, computability, complexity, and computer architecture and also collaborating with data engineers is essential for building data and model pipelines, as well as managing the infrastructure needed for code production. 

As a data scientist at SAP Labs India, you will also analyse large, complex datasets, researching and implement best practices to enhance existing ML infrastructure and provide support to engineers and product managers in implementing ML into products.

Work Culture in SAP

SAP’s work culture is characterised by abundant learning opportunities and hands-on experiences where employees have chances to shadow leading data scientists, participate in fellowship projects for stretch assignments, and explore various aspects. This hands-on approach extends to customer interactions and pre-sales experiences. 

“These opportunities, along with the focus on learning and customer engagement, give SAP an edge over other organisations hiring in data science and machine learning,” Mohanty commented.

SAP prioritises its employees’ well-being through a comprehensive set of benefits and rewards. The company recognises diverse needs beyond healthcare and retirement plans, offering global and local options for work-life balance, health and well-being, and financial health. 

Embracing a highly inclusive and flexible culture, the company promotes a hybrid working model allowing employees to balance office and remote work. Employee Network Groups foster a sense of community, and inclusive benefits include competitive parental leave and disability support. 

The ERP software giant also aims to foster personal and professional growth, providing learning opportunities, career development resources, and a leadership culture focused on doing what’s right for future generations. It values fair pay, employee recognition, generous time-off policies, variable pay plans, total well-being support, and stock ownership opportunities for all employees.

Why Should You Join SAP Labs?

SAP Labs offers a sense of purpose and involvement in transformative technology phases. At SAP, candidates dive into cutting-edge technologies, explore diverse industries, and embrace continuous learning and innovation. 

Mohanty explained how the team values adaptability, emphasising fungible skills and a proactive mindset, especially in areas like AI and generative AI. 

“We seek individuals ready to tackle new challenges and solve complex problems, fostering a dynamic and impactful work environment,” she explained. 

Adding on to what Mohanty said, “The work at SAP involves mission-critical applications, like supporting cell phone towers or vaccine manufacturing so the integration of generative AI into these applications offers a unique combination of purpose and technological advancement, providing developers with a high sense of purpose in seeing their software run essential business and retail operations. This phase of technological transformation at SAP is especially significant for new joiners,” said Karthikeyan. 

Check out the job openings here.

The post Data Science Hiring and Interview Process at SAP Labs India appeared first on Analytics India Magazine.

California-based ServiceNow, one of the leading names when it comes to operating as a cloud-based company delivering Software as a Service (SaaS) has brought purpose-built AI with the highly intelligent NOW platform.  Last September, the company expanded this using a domain-specific ServiceNow language model designed for enterprise use.

The NOW platform converts machine intelligence into practical actions, aiming to enhance process efficiency, reduce risks, optimise workforce productivity, and facilitate automated workflows with the help of purpose-built AI, providing users with self-solving capabilities through augmented intelligence

“With this (NOW Platform), we are enabling enterprises to increase process efficiency, minimise risk by avoiding human mistakes, optimise workforce productivity to focus on higher value tasks​, leverage automated workflows to drive standardisation and empower users to self-solve with augmented intelligence,” Sumeet Mathur, vice president and managing director of ServiceNow’s India Technology and Business Center, told AIM. 

The company has eight open positions in data science.

Applied research scientists in the Core LLM Team focus on developing generative AI solutions, collaborating with diverse teams to create AI-powered products and work experiences. Simultaneously, they conduct research, experiment, and mitigate risks associated with AI technologies to unlock novel work experiences. 

On the other hand, as a machine learning engineer, you’ll craft user-friendly AI/ML solutions to enhance enterprise services’ efficiency, emphasising accessibility for users with varying technical knowledge. 

Inside ServiceNow’s AI & Analytics Lab

The Now platform aims to create proactive and intelligent IT processes. The platform is built around big data and advanced analytics, incorporating real-time and stored data to enhance accessibility and support various use cases, such as self-service, incident detection, pattern discovery, knowledge base optimisation, workflow automation, and user empowerment. 

ServiceNow’s self-service has evolved with augmented AI and automation, using intelligent virtual agents to understand customer intent and resolve complex issues. Augmented agent support focuses on improving human capabilities through recommendation engines, automated workflows, and increased productivity, aligning with specific business objectives for measurable value.

Tapping into Generative AI

Last September, the company expanded its Now Platform using a domain-specific ServiceNow language model designed for enterprise use, prioritising accuracy and data privacy. The Now LLM incorporates top-notch foundational models, including a pre-trained model called StarCodel, developed in collaboration with Hugging Face and a partnership with NVIDIA, along with other open-source models. 

The initial release of Now LLM introduces features such as interactive Q&A, summarisation capabilities for incidents/cases and chats, and assistive code generation for developers. The development of this model involved significant efforts from engineering, research, product, QE, and design teams, as well as data centre operation teams managing the GPU infrastructure. 

Clients like Mondalez, Delta, Standard Chartered, Coca Cola, LTIMindtree, and various other companies across industries have used the platform for AI applications in areas like improving healthcare workflows, providing financial auditors with quick insights, and transforming supply chain management in manufacturing. 

“We believe that the most constructive and value-creating strategies for generative AI are grounded in embedding human experience and expertise into its core capabilities,” added Mathur. 

So it adopts a humans-in-the-loop model for generative AI, integrating human expertise into its core capabilities. The NOW platform’s generative AI is applied in diverse use cases, including case summarisation, content generation, conversational exchanges, and code generation. 

Interview Process

“Our hiring process for data science roles follows a structured approach aimed at attracting a diverse pool of qualified candidates. We publish job openings on various platforms, including our career site, job boards, social media, and professional networks,” added Mathur. The process involves careful evaluation through interviews to ensure the selection of the right candidate. 

The interview process consists of three technical rounds, each focusing on key competencies such as programming proficiency and experience with core ML and LLM. This assessment is followed by an interview with the hiring manager and, for certain roles, an additional round with the senior leadership. 

However, Mathur shared that during the data science interview process, candidates often make common mistakes that should be avoided. Some of them include inadequate technical readiness, a limited understanding of the company’s objectives and role, failure to ask insightful questions, overlooking the latest AI/ML trends, and neglecting to demonstrate effective problem-solving skills. 

Expectations

Upon joining the data science team at the Advanced Technology Group (ATG) of ServiceNow, candidates can expect to work within a customer-focused innovation group. The team builds intelligent software and smart user experiences using advanced technologies to deliver industry-leading work experiences for customers. 

The ATG comprises researchers, applied scientists, engineers, and product managers with a dual mission: building and evolving the AI platform and collaborating with other teams to create AI-powered products and work experiences. The company expects that team members will contribute to laying the foundations, conducting research, experimenting, and de-risking AI technologies for future work experiences.

Work Culture

“Our company fosters a purpose-driven work culture where employees have the opportunity to be part of something big. We make work better for everyone—including our own. We know that your best work happens when you live your best life and share your unique talents, so we do everything we can to make that possible for our employees,” Mathur added.

Some of the key perks include a hybrid working model, paid time off, well-being days, employee belonging groups, DEI learnings, internal opportunities, and paid volunteering.

According to him, joining ServiceNow means becoming part of an inclusive and diverse community with resources for well-being, mental health, and family planning, among others. Prioritising value and trust, SaaS giant provides ongoing support for learning and development, growth pathways, and action-oriented feedback aligned with clear expectations. The programs cater to individuals at all career stages. 

“We’re committed to creating a positive impact on the world, building innovative technology in service of people – with a core set of values and a deep responsibility to each other, our customers and our global communities,” he concluded.

Check out the careers page now.

The post Data Science Hiring and Interview Process at ServiceNow appeared first on Analytics India Magazine.

In February of this year, Razorpay, a prominent fintech unicorn, unveiled Razorpay RAY, a generative AI-powered assistant, for integrated payment and payroll management solutions specifically tailored for e-commerce businesses. 

Leveraging GPT models through Azure APIs, RAY facilitates interactions via voice and text commands on platforms such as WhatsApp and web bots. It serves dual purposes within Razorpay: externally, it assists merchants by enhancing their understanding of data, and internally, it supports the company’s knowledge bases as a QnA service. 

Around the same time, it also launched Payment Gateway 3.0, establishing itself as the only Payment Gateway in India that improves the payment process and the entire buyer journey. Powered by its in-house framework, AI-Nucleus, this innovative checkout system is set to improve business conversions by more than 30%, which is expected to lead to higher revenues.

The team behind making this possible is the company’s close-knit six-member AI/ML team, which is categorised into three roles: data scientist, machine learning engineer, and MLOps engineer. 

Razorpay was founded in 2014 by Shashank Kumar and Harshil Mathur, IIT Roorkee graduates. Since then, the company has raised funding from investors like Y-Combinator, Sequoia India, and Tiger Global over several rounds.

The company is expanding its data science team and is looking for a senior machine learning engineer to join its Bengaluru team. 

“At Razorpay, solving for our customers is the core of everything we do. And the one thing that enables us to do that is data,” Murali Brahmadesam, chief technology officer and head of engineering at Razorpay, told AIM in an exclusive interview last week. 

Brahmadesam shared that as a technology-first company catering to a diverse range of businesses, it prioritises the development of scalable and automated products. However, the founding principle of its operational strategy involves a strong emphasis on security and compliance due to its status as a regulated entity. 

Inside the Data Science Team of Razorpay

Razorpay is working towards AI and data democratisation, fundamentally changing how engineers and data scientists work. 

“This redefines the roles of our engineers and data scientists, allowing every engineer at Razorpay to become a ‘citizen data scientist’,’ using data-driven insights and AI tools in their daily tasks,” said Brahmadesam, highlighting that democratisation is key to creating a collaborative environment. 

“Moreover, our data science team is actively working on platforms using AI model preparation. This initiative is designed to streamline and standardise the process of building generative AI and predictive models, making it accessible for our engineering teams to develop new models independently,” he commented.

Razorpay leverages generative AI models primarily for fraud detection, risk assessment, and personalised marketing. Additionally, it is expanding its AI capabilities to improve document processing across various Indian languages, enhancing its service reach and operational efficiency. 

“While our system excels at processing English documents, we recognise the need for improvement in handling other Indian languages. Fortunately, initiatives like Bhashini are underway to address this,” he added. `

Tech Stack

Razorpay employs a mix of tech solutions for various aspects of its operations, ranging from prototyping to deployment. The company uses Databricks and EMR for these purposes, providing both managed and self-serve options. The data infrastructure includes AWS-managed Kafka for the streaming layer, RDS/Aurora for the batch layer, and S3 for the lake layer. Kubernetes is used for distributed deployments through standard GitHub CI/CD and Spinnaker manages the deployment process. For the managed side, Datarobot is used for both development and deployment tasks.

“We have also explored fine-tuning smaller Falcon and Phi models internally for specific use cases and we will continue to pursue them as well,” he added. 

Interview Process

“To ensure that we are able to hire the right talent and that the candidate also fully knows what is expected of them at the job, we follow a five-step process when recruiting for data science and ML roles,” said Brahmadesam. 

The process starts with a screening and exploratory call, during which candidates’ experience and understanding of the role are assessed, providing a mutual opportunity to explore fitment. This is followed by a weekly data exercise, during which candidates must solve a case study to demonstrate their problem-solving skills and ability to translate a problem statement into a data solution; this solution is then independently evaluated by two engineers. 

The third step involves a coding test focused on Python, SQL, and Pyspark skills through progressively challenging problems. Next, the system design and ML depth interview assesses candidates on their machine learning knowledge and their ability to design systems, such as a real-time ranking engine for payment gateways. Finally, the hiring manager round focuses on cultural fit and levelling considerations, if needed. 

However, he noted that candidates often make common mistakes while interviewing. 

“A lot of candidates are unable to properly chalk out their work experience and have their skills sufficiently reflected in their work resume,” he added, stating that this leads to lesser chances of them qualifying for the interview rounds. 

Expectations

When joining the Razorpay data science team, new hires can anticipate an initial period filled with knowledge sharing, induction sessions, introductions to team members, and brainstorming activities. This phase is designed to integrate them smoothly into the team and familiarise them with the company’s culture and operational methods. 

Gradually, new team members will be assigned specific tasks, where they’re expected to take full ownership and contribute to collaborative efforts to address customer pain points through innovation. 

On the other hand, Razorpay expects more than just adherence to established processes from its new hires. The company values fresh perspectives and encourages its team members to share their ideas freely, without fear of judgement.

“The enthusiasm and passion to innovate and imagine beyond the ordinary is what we most definitely expect them to have when they become a part of the Razorpay family,” Brahmadesam noted.

Work Culture

“As an employee-first organisation, our policies, initiatives, and efforts, are always chalked out with the intent of co-creating a space where employees feel valued, respected, and nurtured,” said Brahmadesam.  

The company’s culture is founded on transparency, questioning the status quo, integrity with agility, customer obsession, and mutual growth with its employees (“Razors”). It maintains a hybrid working environment.

It offers several unique perks, such as health insurance for same-sex and live-in partners, a Family Assurance Benefits Policy, and as well as offbeat initiatives like ‘Bring Your Children & Pets to Work’ initiative. 

The company also supports women re-entering the workforce with its ‘Resume with Razorpay’ programme and offers open hours for mental health counselling. It has also conducted one of the largest ESOP buyback sales in India’s startup ecosystem, which includes both current and former employees. Recreational facilities like foosball, chess, and TV rooms are available at office locations to enhance employee well-being.

Razorpay’s work culture is distinct from its competitors, especially in the way it integrates core values across all job functions, including the data science team. Data scientists at the team have full ownership of their projects. 

“The environment at Razorpay is one where data scientists are not just contributors but are decision-makers,” he added.

This culture of ownership, coupled with a strong emphasis on empathy and employee empowerment, sets Razorpay apart, reflecting its commitment to both individual and company growth.

“Joining Razorpay wouldn’t be like just having a day job but having the massive opportunity to gain that immersive experience of being a part of India’s fintech revolution,” Brahmadesam  concluded. 

Check out Razorpay’s careers page now. 

The post Data Science Hiring and Interview Process at Razorpay appeared first on Analytics India Magazine.

Back in June, Indian IT services and consulting giant LTIMindtree introduced Canvas.ai, a generative AI platform to accelerate concept-to-value realisation for enterprises, all while adhering to ethical AI principles.

Since then, the Mumbai-based subsidiary of L&T has been making strides in building in-house expertise and infrastructure, allowing their data scientists, AI specialists, and MLOps engineers to engage in R&D projects exploring the potential of LLMs. Moreover, they offer consulting services to guide clients in integrating generative AI into their processes, from use case identification to model deployment. 

“Our strategy for generative AI revolves around a combination of in-house expertise development, research and experimentation and client-centric application to deliver AI services that drive value and solve real-world challenges,” said Jitendra Putcha, EVP– Data, Analytics & AI, LTIMindtree told AIM. 

With over 20 years in the industry, Putcha is known for solving data challenges globally, promoting next-gen solutions, and leading generative AI initiatives. His career includes leadership roles at Cognizant, focusing on data modernisation, AI, and analytics. AIM spoke to him about LTIMindtree’s AI initiatives, hiring strategy for data science candidates, work culture and more. 

The company is actively hiring for different positions in its AI team. 

Inside LTIMindtree’s Data Science Lab

The team addresses various core challenges through data science and AI solutions, including enhancing customer experiences by delivering personalised services, optimising operations by automating tasks and improving decision-making, managing risks by identifying anomalies and potential issues, and exploring new opportunities for growth and competitiveness. 

One specific example of their work involves assortment planning with APEX Solution in the retail consumer industry, which optimises product placement on retail shelves to maximise sales and enhance related product positioning. 

The organisation implements AI and data science by consulting with businesses to identify and prioritise AI use cases, gathering data to achieve actionable insights, and scaling solutions using hyper scaler cloud platforms. They emphasise responsibility by ensuring unbiased AI models. 

Strategically, the organisation focuses on various generative AI architectural styles, including using commercial and open-source APIs for tasks like language translation and code conversion, fine-tuning foundation models using their Canvas.ai platform for secure data, employing RAG for real-time data incorporation, and co-investing in building foundation models when access to proprietary data is available through key customer affiliations. 

Interview Process

“We are looking for candidates with skills in data science, AI, and ML, with a strong expertise in deep learning and NLP, in-depth domain knowledge, proficiency in Python programming, a learning mindset conducive to prompt engineering work, effective communication skills, and the ability to stay current with the latest trends for adoption,” said Putcha, talking about the interview process at LTIMindtree.

Internally, the company employs an automated screening and assessment platform called WeCP to evaluate candidates before they can apply for AI-related positions. 

“To identify exceptional AI talent, especially among the GenAI’s demographic, we engage AI veterans to lead evaluation panels and mentor participants in nationwide hackathons like the Smart India Hackathon and the Singapore-India hackathon,” he added.

The interview process for a lateral talent hunt includes multiple rounds: the screening round, the first technical assessment round, the second comprehensive technical round, and the final round focusing on company culture and values, often involving discussions with the HR team. This well-structured process ensures a holistic assessment of candidates before making a final hiring decision.

However, Putcha elaborated on the most common mistake candidates make when interviewing for a data science role in the company. He said that candidates often neglect to refresh their knowledge of key data science concepts and Python programming skills. Additionally, some candidates struggle to articulate domain-specific business problems and demonstrate how their solutions align with broader outcomes and impacts.

Expectations

When candidates join the data science team at the company, they can expect to serve as strategic advisors to clients, leveraging proprietary platforms and products to drive substantial business improvements such as increased revenue, reduced total cost of ownership, optimised operations, and enhanced fraud and risk detection. 

Alongside this, employees will have a plethora of upskilling opportunities that encompass a comprehensive approach to employee growth and AI proficiency. These initiatives include a career framework called ‘My Career My Growth’ for career progression, a focus on skill development with the Shoshin School offering over 5,000 courses and a Hub and Spokes model for AI content incubation. 

Work Culture

The company’s work culture is characterised by a strong focus on its people, emphasising employee well-being, empowerment, and societal impact. “This culture is driven by employee-friendly policies, flexible work arrangements, and a performance-driven approach,” Putcha explained. 

“What sets us apart from competitors, especially for the data science team, is its unique positioning as both nimble and financially robust. Our solutions are oriented towards benefiting society, making it a compelling and inspiring place to work,” said Putcha. The Yin-Yang model allows for flexibility in work arrangements, with a strong emphasis on continuous learning and innovation. 

In terms of diversity, the gender ratio in the AI team is approximately 30%. The company is committed to promoting diversity, equity, and inclusion, creating a safe and inclusive environment for differently-abled employees as part of its DEI Charter. 

“Our dynamic learning environment empowers AI specialists to excel in their field, while our unique culture, focus on customer centricity, and innovation-driven approach provide a platform for making a meaningful impact in the industry,” concluded Putcha. 

Read more: Data Science Hiring Process at Happiest Minds Tech

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Founded in 2011 by Ashok Soota, a serial entrepreneur and Indian IT veteran, Happiest Minds boasts a robust data science team comprising over 300 members, including data engineers, intelligence specialists, and data science experts.

Based in the Silicon Valley of India, Bangalore, and extending its reach across the global landscape, including the US, UK, Canada, Australia, and the Middle East, this IT juggernaut seamlessly blends augmented intelligence with the art of understanding human language, deciphering images, analysing videos, and harnessing cutting-edge technologies such as augmented reality and virtual reality.

This dynamic fusion empowers enterprises to craft captivating customer interactions that surpass rivals and set new industry standards.

Happiest Minds distinguishes itself from traditional IT companies by avoiding legacy systems like SAP and ERP, believing that staying entrenched in these technologies limits growth and innovation. “Instead, we have chosen to focus on digital technologies like AI, which is the future of IT,” said Sundar Ramaswamy, SVP, Head of Analytics CoE, in an exclusive interview with AIM.

The team conducts regular market scans to identify the latest technologies and ensures that they are always on the forefront of innovation. This approach allows them to co-create and innovate with clients while building new solutions.

Now Hiring

Happiest Minds is currently on the lookout for a specialist in marketing analytics. The ideal candidate should possess a Master’s or Bachelor’s degree in Computer Science, STEM, or an MBA, demonstrating strong problem-solving skills. They should also have over eight years of experience in the analytics industry, particularly in marketing. 

This experience should include a track record of using AI to enhance the customer journey, encompassing areas such as customer acquisition, nurturing, retention, and improving the overall experience.

The technical skills required include proficiency in statistical techniques, ML, text analytics, NLP, and reporting tools. Experience with programming languages such as R, Python, HIVE, SQL, and the ability to handle and summarise large datasets using SQL, Hive-SQL, or Spark are essential.

Additionally, the knowledge of open-source technologies and experience with Azure or AWS stack is desirable.

AI & Analytics Play

This team collaborates closely with domain teams across diverse industry verticals. Their analytics process follows eight key steps. They integrate data from multiple sources, use BI tools for descriptive analytics, perform ad hoc analysis, build data pipelines and auto ML pipelines, retrain models regularly, focus on customer understanding, optimise cloud usage, and ensure data governance.

Their key industry verticals are CPG retail, healthcare (bioinformatics), FSI, media entertainment, and Edtech, with growing interest in manufacturing. The team works with classical analytics, deep learning, computer vision, NLP, and generative AI. This includes advanced applications like language translation and content generation from 2D to 3D images using generative AI.

Recognising the growing importance of generative AI, they have formed a dedicated task force comprising approximately 50 to 60 members, drawn from diverse domains, under the leadership of their CTO with the primary objective to leverage generative AI in addressing industry-specific challenges.

To achieve this, they’ve identified and categorised 100 to 250 distinct use cases across ten different domains, tailored to the specific requirements of each domain. The team is diligently working on creating demos and proof of concepts (POCs) that are domain-specific. 

Some team members come from analytics backgrounds, contributing their technical expertise, while others from domain areas contribute to shaping ideas and ensuring results align with the industry’s needs. This undertaking is substantial for the organisation, considering they have around 5,500 employees, with 100-160 dedicated solely to generative AI. 

In addition to building demos, the company is also focusing on educating its entire workforce about LLMs and their applications to equip all team members with a basic understanding of generative AI’s capabilities and potential applications.

To bring generative AI into action, the company is working with Microsoft’s suite of products. “We are a Microsoft select partner and are also experimenting with different language models,” he added.

The team initially experimented with Google’s BERT and now employs models like GPT-2. They have a strategic inclination towards refining existing models to suit specific applications, rather than developing entirely new foundational models. For example, they collaborate with a healthcare company to craft adaptive translation models with reinforcement learning.

Interview Process

“Data science is not just about technical skills; it also involves an element of art. Candidates are assessed on their ability to communicate their results effectively and their capacity to approach problems with creativity,” said Ramaswamy.

The interview process for data science candidates at Happiest Minds typically involves three to five levels of interviews. The first level is a screening by the HR team based on the job description. This is followed by a written test to assess the candidate’s proficiency in relevant languages and skills. For example, if the position is for a data engineer, the test might evaluate their ability to work with SQL and other database-related tasks. 

Technical interviews are conducted using case studies to evaluate the candidate’s problem-solving ability and approach. The interview process concludes with a leadership interview, especially if the position is a senior one.

In addition to understanding the interview process, candidates often wonder about the common mistakes they should avoid. According to Ramaswamy, there are two main pitfalls that candidates often fall into. First, many candidates focus excessively on specific tools or techniques and become fixated on mastering them.

“While technical proficiency is essential, it’s equally important to explain the problem being solved, the reasons for approaching it a certain way, and considering alternative solutions,” he added.

The second common mistake is becoming too narrowly focused on the solution without understanding the broader context. It’s crucial to see the big picture, why the problem is being solved for the client, and to ask relevant questions about the projects they’ve worked on. 

In terms of skills, the company looks for both technical and non-technical abilities. The specific skills depend on the role of the position, such as data engineering, business intelligence, or data science. 

However, primary technical skills include proficiency in relevant tools and technologies, certifications, and problem-solving abilities. Non-technical skills are communication and presentation skills, problem-solving skills, and the ability to coach and mentor, as collaboration and teamwork are essential for senior positions.

Work Culture

“As the company’s name suggests, we aim to cultivate a distinctive work culture based on four fundamental pillars,” Ramaswamy commented. Certified as a Great Place to Work, the company prioritises the well-being of their employees, believing that “a content workforce leads to happy customers“. They monitor and maintain employee happiness closely, offering support to those facing personal or professional challenges.

Collaboration is another key element of their culture, as they encourage a unified approach within and across different units and locations. “As a company born in the digital age, Happiest Minds thrives on agility, adapting swiftly to meet the ever-changing needs of customers and the digital industry,” he added.

Transparency is the fourth pillar, as they openly share key performance indicators and objectives with their employees, investors, and stakeholders. This culture of transparency and goal-oriented approach ensures that their efforts are always aligned with clear objectives and tracked diligently.

If you think you fit the role, check out their careers page now. 

Read more: Data Science Hiring Process at PayPal

The post Data Science Hiring and Interview Process at Happiest Minds Tech appeared first on Analytics India Magazine.

American financial services giant PayPal’s approach to implementing and leveraging data science is driven by the growing demand for personalised customer experiences as it helps in predicting customer actions and streamlining payments, while data analytics provides valuable insights into behaviour and preferences, enabling tailored experiences.

“We put this through features such as faster checkouts, preferred modes and currencies, saved payment methods, real-time updates, and diverse pricing options that detect fraud and protect sensitive data,” said V. Chandramouliswaran, Vice President of Data & Site Leader for PayPal India, in an exclusive interaction with AIM. 

PayPal originated from the birth of Confinity in December 1998 by Max Levchin, Peter Thiel, and Luke Nosek, initially focusing on security software for handheld devices. After an unsuccessful venture, Confinity shifted its emphasis to a digital wallet, launching the first version of PayPal in 1999.

In March 2000, Confinity merged with x.com, an online financial services company founded by Elon Musk in March 1999. Musk, optimistic about Confinity’s money transfer business, clashed with Bill Harris, who left the company in May 2000. Musk decided to refocus x.com on payments in October of that year, with Peter Thiel replacing him as CEO. 

Renamed PayPal in June 2001, the company went public in 2002, generating over $61 million in its IPO listed under the ticker PYPL at $13 per share. Since then, the company has been heavily investing in cutting-edge technology to provide better solutions to customers. 

Now Hiring

PayPal is seeking qualified individuals for three distinct roles within the team – data scientists in product analytics, paid marketing and data scientists (managers).

First, in the role of data scientists in product analytics, they will conduct comprehensive data analysis, delving into large, multi-dimensional datasets to derive valuable insights. The ideal candidate should possess three to five years of experience in this capacity and demonstrate proficiency in SQL, Excel, and visualization tools like Tableau or Qlikview.

Additionally, expertise in a statistical programming language such as R or Python is preferred. Specialised knowledge in payments or other consumer financial products, along with experience in product analytics and experimentation (A/B testing), is highly desirable.

Simultaneously, they are recruiting data scientists in paid marketing, seeking individuals with a background in analytics, data science, or management consulting, or an equivalent blend of analytical and project management expertise.

Candidates should have hands-on experience with Python, SQL, and BigQuery, and be accomplished contributors capable of managing high-performing data scientists and quantitative analysts. Specific experience in tracking and measurement within paid media is a key requirement for this role. 

Finally, Data scientists and data scientist managers should have proven analytical skills, demonstrating proficiency in SQL and data visualisation. They should excel in leading cross-functional collaborations, and effectively coordinating among multiple stakeholders. Their expertise extends to understanding business considerations, enabling them to enhance decision-making methodologies. 

What’s Cooking inside PayPal’s AI Kitchen

“We are developing AI models and generative AI products for both internal use and customers,” said Chandramouliswaran, boosting the daily operations. The company is committed to democratising financial services’ access to everyone by relying responsibly on AI and automation.

As for generative AI, PayPal is actively exploring various use cases such as leveraging co-pilots for faster software development, creating internal and external chatbots that use their own data ecosystem to predict customer needs, engaging in compliance-related activities for a deeper understanding of consumers and sellers, and strengthening defenses in both the risk and cyber space. 

Acknowledging that generative AI presents both opportunities and challenges, especially in security programs enabling malicious actors to create fake identities and sophisticated malware, Chandramouliswaran commented, “On the flip side, firms can use generative AI to enhance defense by deploying automated threat detection systems and adaptive security protocols,”

Interview Process

“The core to our hiring strategy for data science roles lies in functional skills and business acumen,” said Chandramouliswaran.

PayPal seeks candidates with strong problem-solving skills for real-world data science challenges. Key functional skills include expertise in machine learning, OpenCV, and deep learning, with a preference for experience in payments, banking, risk assessment, customer management, and marketing.

While a foundation in programming, statistics, economics, and mathematics is important, logical reasoning, data interpretation, and a programming-oriented mindset are crucial. The team values experience and exposure over specific educational backgrounds, as long as they understand the significance of their contributions and believe in a collaborative approach to work. 

“Our aim is to bring in individuals who consistently challenge and inspire us, propelling us to innovate on a daily basis,” he commented. 

When it comes to what the company will help the candidate with, they can expect an inclusive and diverse work culture where practical problem-solving skills, curiosity, understanding of business objectives, and teamwork are valued. Alongside, alignment with PayPal’s core values is essential for candidates, contributing to the company’s mission. 

However, candidates often make a common mistake during their interviews – 

Often they approach it with a narrow perspective, fixated on a specific tool or method, “a hammer and looking for a nail,” in the words of Chandramouliswaran. Instead, they should prioritise understanding the “why” behind a problem, and its significance, understanding the potential benefits for the company, and then strategically delving into the “how” to address it. 

Responsibilities for data scientists include problem structuring, data preparation, model development, validation, and collaboration with business and product teams. Comfort with ambiguity is expected due to the complex nature of challenges, and the ability to define impactful problem statements is considered a differentiating factor.

Work Culture

PayPal’s Global Technology Centres in Bangalore, Chennai, and Hyderabad constitute the company’s largest facilities outside the US, with India-based employees playing a crucial role in advancing their global mission. Teams in India have made significant contributions across various domains, leading to over 300 patent applications.

The work culture prioritises the collective responsibility of employees in realising the company’s mission and culture. Innovation, a core value, is fostered by providing a secure environment for learning, skill enhancement, and experimentation. 

The company is committed to creating an inclusive, diverse and engaging work environment by actively listening to employees, expanding the talent pipeline, crafting immersive experiences, and promoting well-being. 

Employee Resource Groups (ERGs) connect diverse employees and allies, fostering cultural change while Community Impact Teams, locally-led, strengthen relationships between PayPal offices and their communities through volunteering and grantmaking.“We differentiate ourselves through this approach,” said Chandramouliswaran. 

Another interesting pillar of PayPal promoting innovation is through their Global Innovation Tournament, aligned with their three-horizon approach. The tournament involves multiple phases, including business plan development, prototyping, and pitching to PayPal’s leadership. In the 2022 edition, nearly 540 submissions were received, with two of the top three finalists originating from India. The tournament serves as a learning platform, encouraging employees to hone new skills in a secure environment.

“PayPal’s global presence empowers businesses and individuals to give better control of their finances. Our success is driven by a purposeful mission and a commitment to developing its employees as a key competitive advantage,” concluded Chandramouliswaran. 

Click here to apply. 

The post Data Science Hiring and Interview Process at PayPal appeared first on Analytics India Magazine.

For Indian SaaS unicorn Zoho, AI has grown to be an integral part of its operations. In 2011, it began its journey with data science and has now developed its complete technology infrastructure internally, spanning from data centres to AI software tools. This in-house ingenuity empowers Zoho to effectively address various business obstacles encountered by its clientele and users.

“Our goal is to democratise access to AI for businesses of all sizes, significantly lowering the entry barrier in terms of cost and the volume of data needed for training,” said Ramprakash Ramamoorthy, director – AI Research at Zoho, in an exclusive interaction with AIM.

With its headquarters in Chennai, the company recently crossed 100 million users across its expansive suite of over 55 business applications, without relying on external investments, becoming the first bootstrapped SaaS firm to achieve this milestone. This comes on the heels of Zoho’s remarkable accomplishment of reaching $1 billion in annual revenue the previous year.

Founded in 1996 by Sridhar Vembu and Tony Thomas, Zoho has evolved into a worldwide frontrunner in delivering cloud-centric software solutions tailored for both enterprises and individuals. The firm boasts an extensive portfolio of applications and services that encompass diverse facets of corporate functions, such as managing customer relationships (CRM), facilitating email and collaborative tools, enhancing office productivity software, streamlining financial and accounting processes, automating marketing endeavours, and much more.

Currently Hiring

Zoho has over 10 job openings in AI and ML across various locations. These roles require a strong knowledge of algorithms, machine learning, and experience with handling large datasets. Responsibilities include collaborating with teams to identify AI and ML applications, developing models from concept to production, data collection and analysis, using ML techniques to solve real-world problems, optimising models for performance, and staying updated in the AI field.

Both seasoned professionals with a profound understanding of data analytics, data visualisation techniques, and coding skills, as well as recent graduates eager to learn and contribute to challenging projects, are suitable for these positions. The desired qualities in potential candidates include a track record of past achievements, a forward-looking attitude towards accomplishing tasks, and a penchant for collaborative teamwork and ownership.

Inside Zoho’s Data Science Team

At the heart of Zoho’s AI efforts is its adaptable AI research team, which operates within the company’s hybrid AI infrastructure, allowing it to swiftly respond to market demands and promote innovation. The AI research team is a vital part of Zoho’s central research hub, ZLabs, which influences strategic AI, analytics, and other critical areas. Zoho collaborates closely with its central AI team and product-specific AI experts to implement tailored AI solutions. 

Initially, AI was used for tasks like finding unusual patterns and understanding sentiments. Today, AI is deeply integrated into Zoho’s technology, powering various features in its products. These AI-driven features include statistical ML, computer vision, natural language processing, and predictive analytics for business users. Importantly, Zoho emphasizes user privacy when developing and using AI models.

AI, ML, and data science are not just for product development at Zoho; they also play a significant role in the company’s IT operations. In IT monitoring, AI steps in to find anomalies, anticipate potential problems, and pinpoint the origins of issues. Zoho harnesses the power of AI not only to scrutinize operational data for more accurate forecasts but also to trim costs. On another front, conversational AI interfaces grant instant access to vital organizational data, making the process of handling tickets a breeze. And Zoho doesn’t stop there; they put AI to work in bolstering security by actively detecting and preventing malware, keeping an eye on user behavior, and spotting phishing threats before they cause havoc.

Riding the Generative AI Wave

Simultaneously, the company is also experimenting with generative AI. Earlier this year, Zoho stated that it’s increasing its investment in R&D and plans to introduce additional generative AI solutions gradually between 2023 and 2024. They explore various avenues for the implementation of generative AI. Within its AI strategy, three fundamental principles—Privacy, Experience, and Value—guide its approach to integrating generative AI technologies. 

Zoho has integrated its AI engine, Zia, which operates securely on the Zoho cloud, with OpenAI’s ChatGPT. As a result, a total of 13 generative AI applications and integrations, all powered by ChatGPT, are now available within the Zoho ecosystem, solidifying ChatGPT’s growing influence in the Zoho landscape. 

One notable feature, “Ask Zia,” empowers users to inquire about its data, with AI providing responses based on its comprehension of the data. Moreover, “Zia Presentations” can generate statistics and reports using user data from CRM. Its use of generative AI extends to tasks such as automated translation, grammar error detection, and more.

“The collaboration with OpenAI has been pivotal in enabling Zoho customers to embrace generative capabilities by simply adding the OpenAI key to its accounts. This integration ensures that sensitive organizational context remains securely within the confines of its accounts, a crucial aspect from a privacy perspective,”

In June, Vembu said that Zoho is also preparing to build its proprietary LLM, with the company’s devoted R&D unit,  where data will always be retained within the Zoho ecosystem.

Tech Stack

Zoho relies on its own suite of products and internal tools to enhance customer performance. Initially, Java was the primary programming language, favored for its compatibility with operational procedures and Zoho’s unique infrastructure, which uses co-located datacenters rather than public cloud providers like AWS or Azure.

Over time, Java remained the choice for statistical machine learning models, while Python gained popularity for neural networks due to open-source frameworks. Zoho also maintains native models in C for on-premise and mobile integration. They use their proprietary language, Deluge, across over 55 applications, enabling tailored solutions for diverse customer needs. 

Hiring Process

The hiring process for data science roles at the company focuses on technical skills, problem-solving abilities, and alignment with company values. For entry-level positions, it includes a programming and aptitude test, multiple rounds of programming interviews, and personal interviews to evaluate communication skills and cultural fit. Lateral hires undergo a customized process based on experience, involving programming assessments, discussions on data science concepts, and presentations on past challenges.

Key skills valued by the company include a strong programming foundation in Java, Python, and C.

Upon joining the data science team, candidates can expect a positive and open work environment that encourages ownership, personal development, and creativity. Zoho expects team members to bring their best skills, commitment, and passion, fostering growth, innovation, and collaboration through experimentation and learning from mistakes.

Tips To Crack The Interview

Prospective data science candidates should approach interviews with honesty and preparation, emphasizing a strong foundation in fundamental concepts and programming skills. The company values technical proficiency, personality, values, and potential contributions during the interview process.

Key messages for candidates applying for data science jobs at the company:

• Deep understanding of fundamental concepts is essential for success.
• Proficiency in programming languages is crucial.
• Hands-on practice with real-world data is encouraged.
• Utilize available digital resources for learning and growth.

The company prioritises privacy as a fundamental right and ethics in innovation. It values qualities like curiosity, creativity, and a commitment to problem-solving in prospective candidates. Those who share these values are encouraged to apply.

Work Culture

Zoho’s work culture is distinct in its rejection of the hustle culture in favor of a supportive and enriching environment. It centers around two core pillars: people and research and development (R&D). The primary focus is on fostering individual growth and contributions, with transparency and an open-door policy ensuring all team members feel valued. This approach encourages teamwork and community spirit.

Furthermore, Zoho’s work culture places a strong emphasis on R&D and technical knowledge. Employees enjoy various perks like 24/7 food availability, comprehensive family health insurance, transportation benefits, and paid time-off. While ESOPs aren’t offered due to private ownership, the company has a profit-sharing plan to reward employees based on company success. 

In terms of gender diversity, Zoho takes a non-traditional approach, prioritizing talent alignment with values rather than traditional qualifications. Notably, the Zoho Labs division achieves a balanced 50:50 gender ratio, showcasing the company’s dedication to diversity and inclusion.

What sets Zoho apart from competitors is its emphasis on autonomy, long-term growth, values over metrics, community commitment, privacy, innovation, R&D focus, and openness. 

Check out the opportunities now. 

Read more: Data Science Hiring Process at Digit Insurance

The post Data Science Hiring and Interview Process at Zoho appeared first on Analytics India Magazine.

When it comes to insurance, prolonged delays in processing data and the need for human involvement in extracting and confirming information have posed significant obstacles since time immemorial. These manual procedures not only demand a hefty resource stack but are also error-prone, resulting in bottlenecks and operational inefficiencies. That is when Kamesh Goyal’s full-stack insurance company Digit General Insurance came into the picture to address this challenge.

Digit Insurance’s robust data platform enables real-time data processing that aids in decision-making, risk assessment, fraud detection, and customer engagement. “Technology is the backbone of Digit and over the years we have embedded various tech innovations into the systems that have aided in a complete overhaul of how insurance products are experienced by Indian customers today,” Vishal Shah, head of data science, Digit General Insurance told AIM. 

And to make this possible, Digit Insurance boasts a big AI and analytics team with over 130 employees distributed across distinct divisions such as analytics, data science, business analysis, and API Integration. Based in Bengaluru, the general insurance firm achieved a unicorn status in 2021, a mere five years following its launch, thanks to a $255 million funding round led by General Atlantic and Multiples Private Equity. It is also backed by the likes of Sequoia Capital India, Fairfax Group and TVS Capital Funds. 

AIM got in touch with Amrit Jaidka Arora, chief of human resources, Digit, and Vishal Shah to understand their data science applications, hiring process, work culture and more. 

Currently Hiring

Digit Insurance is expanding its data science team and is looking to hire AI/ML engineers and BOT developers. They are also looking for data engineers (data warehousing and data lake teams) as well as visualisation developers. Furthermore, they are actively seeking individuals with a robust understanding or a strong passion for insurance and API integration to join their team in a business analyst + technology-oriented role.

Inside Digit’s AI & Analytics Lab 

The organisation leverages AI and ML extensively in its operations, with a focus on its robust data platform that securely stores diverse structured and unstructured data. This platform integrates data from various sources, like customer interactions and transaction records, for analysis, particularly crucial for real-time applications like fraud detection. Data engineers play a pivotal role in ingesting and transforming data through ETL processes, ensuring quality, and optimising query performance. 

“The team also uses NLP to streamline document processing, reducing turnaround times and aiding in fraud detection. Their AI-powered chatbots reduce query resolution times, while computer vision expedites pre-inspection for vehicles and enhances four-wheeler claims assessment accuracy,” added Shah. 

They also harness their extensive datalake for ML algorithms, driving both one-time analysis and real-time solutions based on insights derived from the data.

Tech Stack

The team primarily uses SQL, Postgres, No-SQL database, and Python either through Jupyter Notebook or Visual Studio. Various frameworks like TensorFlow, PyTorch, Hugging Face, NLTK, spaCy, are also used in our processes. 

Transformer or GPT models are often employed for natural language processing tasks like chatbots or sentiment analysis. “When it comes to generative AI, we have initiated pilots on various use cases including chatbot, help desk and more. We are also evaluating various LLMs for their efficacy in providing the results,” commented Shah.

Addressing Ethical AI

The adoption of AI in the insurance and financial sector presents significant ethical and regulatory challenges. According to Shah, the key concern revolves around safeguarding user data privacy and confidentiality, especially when extracting information from customer documents. Compliance requirements necessitate transparent data handling and storage practices, including clear disclosure of data processing and usage procedures. 

Additionally, mitigating model bias is crucial to prevent unfair or discriminatory outcomes. Complying with regulations, particularly in sectors like insurance, demands transparency in decision-making processes, compelling organisations to articulate how their AI models arrive at decisions and predictions to maintain compliance. Addressing these challenges involves robust data protection measures, bias mitigation strategies, and transparent model explanation practices.

Interview Process

“The interview process at Digit begins with an initial round of discussions, either conducted internally or outsourced, aimed at evaluating the candidate’s qualifications. Following this, candidates are given a case study or assignment that assesses their core skills. The final technical discussion is built upon the results of this assessment. Lastly, a third and final round takes place, during which HR conducts an offer discussion with the candidates.

Digit’s hiring process for data science roles focusses on four distinct tech sub-departments: data science (encompassing AI, ML, and BOT), analytics (involving data engineering and data visualisation), API integration, and business analysis. For these roles, the company seeks candidates with a diverse skill set that includes proficiency in Python, SQL, PL, SQL, Qlik Sense, NLP, and Computer Vision.

When quizzed about the common mistake that candidates often make, Arora said, “Candidates often miss reading or understanding the JD in detail and apply for roles that do not really match the profile requirements. Though their CV would mention some of the skills, if they have not worked in-depth on the core skills we are looking at, clearing the interview and technical rounds could be tough.”

Expectations

When joining the data science team at Digit, candidates are introduced to a company culture that is both flexible and transparent, providing a plethora of opportunities for growth and development. “We are known for our diversity, offering the chance to collaborate on significant, scalable projects and engage in cutting-edge initiatives at the forefront of data-driven decision-making, thereby gaining valuable experience in emerging technologies,” said Arora. 

In turn, Digit expects candidates to possess strong coding skills, a robust logical and analytical mindset, and relevant experience aligned with the job descriptions. Candidates should also be prepared to thrive in an agile and dynamic work environment.

Work Culture

“In 2017, we started as the 27th General Insurance Company, knowing the tough competition and industry norms but we saw it as a chance to change the insurance sector. Our approach focused on creating a lasting culture that matches our goal of simplifying insurance. We value challenging conventions and transparency, promoting a positive workplace,” said Arora.

Digit aims to create an open culture based on four key principles. The first is ownership, where employees adopt an owner mindset and take responsibility for addressing feedback and collaborating to improve. The second is people relations, emphasising treating everyone with respect and empathy. The third principle is evolve, highlighting the importance of continuous growth, learning, and mutual support in achieving their mission. Lastly, they embrace a no hierarchy philosophy, promoting accessibility and equality among all employees, fostering a flat organisational structure to encourage simplicity and inclusivity.

In terms of perks and benefits, Digit offers a comprehensive package to its employees, which includes ESOP, various insurance coverage, attendance bonuses for specific departments, health and wellness benefits, relocation support, study assistance programs, car lease support, creche facilities, referral bonuses and much more. “So if have the skills and drive to deliver rapid, innovative, and intelligent solutions while efficiently collaborating with colleagues and customers with cognitive solutions, Digit Insurance is your ideal place for success,” concluded Arora. 

Read more: Data Science Hiring Process at Dream11

The post Data Science Hiring and Interview Process at Digit Insurance appeared first on Analytics India Magazine.

Wipro, which began as a family operated vegetable oil manufacturer in the small town of Amalner, India in 1945, is now one of the largest IT companies globally, functioning in more than 167 countries.

The company has been a key player in driving generative AI. With over 30,000 AI and analytics professionals, the team is building their own LLMs. 

AIM got in touch with Sriram Narasimhan, global head of data, analytics and insights, Wipro, to understand about their data science applications, hiring for these roles, work culture and more. 

Inside Wipro’s Data Science Team

“Data serves as the bedrock for every AI NS ML initiative, laying the groundwork for success. The pivotal factor lies in guaranteeing precise data of optimal quality—an indispensable catalyst for these processes that yield the desired results,” Narasimhan told AIM.

Profound insights emerge from the ability to scrutinise, profile, and decipher patterns within the data landscape, identifying outliers to extract meaningful conclusions. At the heart of any data science endeavour is the adeptness to construct automations through AI and ML algorithms, elevating and refining the data and insight ecosystem.

This transformative process enhances operational efficiencies, underscoring the fundamental role of data science and engineering as the critical inaugural stride in the pursuit of quality outcomes in AI/ML implementations.

Wipro’s AI and analytics team is substantial, with over 30,000 practitioners globally. The company boasts 500+ AI/ML patents, 20 innovation centres, and over 15 partnerships with a strong presence in various industries. 

Recognised as a leader by agencies like Everest Group and IDC, Wipro specialises in industry-specific solutions and horizontal offerings like ML Ops and Legacy modernisation.

“The team co-builds solutions, leveraging tools like the Wipro Data Intelligence Suite (WDIS), prebuilt Industry Business Applications, and the Wipro Enterprise Generative AI (WeGA) Framework,” he added. These tools accelerate customer implementations, supporting the modernisation journey and enabling responsible AI with safety and security guardrails.

Riding the Generative AI Wave

Wipro has been actively involved in generative AI initiatives for over two years, collaborating with research institutes like the AI Institute at the University of South Carolina and IIT Patna. The company is committed to training its sizable workforce of 250,000 in generative AI. They have developed their own LLMs enhancing versatility and future-proofing, and have established a unique partnership with Google Cloud to integrate its generative AI products and services.

The company’s generative AI applications cover diverse themes, including cognitive chatbots, content creation and optimisation for marketing, media, automation in code generation, and synthetic data generation. The company’s internal initiative, Wipro ai360, focuses on incorporating AI across all platforms. Notable client projects include assisting a chocolate manufacturer in enhancing product descriptions and collaborating with a European telecom company to extract value from data.

Wipro is invested in the generative AI landscape, with 2/3rd of its strategic investments directed towards AI. The company plans to further support cutting-edge startups through Wipro Ventures and launch a GenAI Seed Accelerator program to train the top 10 generative AI startups.

Acknowledging the challenges associated with generative AI, the Bengaluru based tech giant has implemented a control framework, emphasising responsible usage. Initiatives include dedicated environments for developing generative AI solutions, GDPR-compliant training, and efforts to detect AI-generated misinformation. They have also established an AI Council to set development and usage standards, emphasising ethical guidelines, fairness, and privacy.

The team is attuned to evolving regulatory frameworks and is adapting strategies accordingly. The company envisions widespread benefits to the IT industry, with generative AI influencing code generation and call centres. The team anticipates a wave of AI services emerging in the next five years, facilitating enterprises in harnessing AI’s full potential. In the long term, they foresee AI disrupting every industry, with specific verticals like precision medicine, precision agriculture, hyper-personalisd marketing, and AI-led capabilities in smart buildings and homes gaining prominence.

Interview process

When hiring for data science roles, Wipro seeks candidates with practical experiences, strong programming and statistical skills, analytical abilities, domain knowledge, and effective presentation skills. 

“The hiring process involves a comprehensive evaluation based on real-world use cases, emphasising not only technical proficiency but also the candidate’s understanding of problem statements and the application of statistical methodologies to solve complex issues,” he added.

“Joining our data science team promises exposure to cutting-edge, real-life AI/ML problems across various industries as we encourage a democratic approach to AI, allowing teams the independence to build solutions while adhering to organisational processes,” Narasimhan commented.

The company offers a diverse range of competencies, including data engineering, data science, conversational AI, ethical AI, and generative AI, enabling associates to work on projects aligned with their capabilities and aspirations.

In interviews, Wipro emphasises the importance of showcasing real-life use cases rather than being overly theoretical. Candidates are encouraged to highlight their practical experiences, demonstrating how they understand, consider options, and provide solutions to problems in the realm of data science, AI, and ML.

Work Culture

Wipro fosters a work culture rooted in values and integrity for its global workforce of 250,000+. Guided by the ‘Spirit of Wipro’ and ‘Five Habits’ principles, it emphasises respect, responsiveness, communication, ownership, and trust. With a 36.4% gender diversity goal, the company supports inclusion through programs like Women of Wipro (WOW), addressing various aspects of diversity such as disability, LGBTQ+, race, ethnicity, and generational diversity.

For talent management, they use tech solutions like the MyWipro app and WiLearn. These tools facilitate goal documentation, feedback, skill-building, and awareness of biases. The company conducts biannual performance reviews, offers training, mentoring, and leadership programs, including global executive leadership initiatives.

Employee benefits encompass a comprehensive package, including 401k, pension, health, vision, dental insurance, competitive pay, bonuses, paid time off, health savings, flexible spending accounts, disability coverage, family medical leave, life insurance, and more. Additional perks involve retirement benefits, stock purchase plans, paid holidays, legal plans, insurance for home, auto, and pets, employee discounts, adoption reimbursement, tuition reimbursement, and well-being programs.

The post Data Science Hiring and Interview Process at Wipro appeared first on Analytics India Magazine.

Pegasystems, commonly known as Pega, is a global software company founded in 1983, focusing on customer engagement and operational excellence solutions. The Cambridge-based company has become a leader in business process management and customer relationship management.

The primary offering, Pega Infinity, acts as a comprehensive platform for businesses to create, implement, and improve applications, aiming to enhance customer experiences and streamline operational processes.

The company utilises AI and data science throughout its platform to improve decision-making, automate processes, and provide personalised customer interactions. CISCO, HSBC, and Siemens are a few of their primary customers. 

In their latest iteration of Pega Infinity 23, the platform introduces over 20 new features, including generative AI-powered boosters to enhance efficiency. The Connect Generative AI feature enables organisations to quickly utilise generative AI with a plug-and-play structure for low-code development.

AIM caught up with Deepak Visweswaraiah, vice president, platform engineering and site managing director, and Smriti Mathur, senior director and head of people, Pegasystems, India, to understand their generative AI play, hiring process and more.

Pega has open positions for solutions engineers and senior software quality test engineers in Hyderabad and Bengaluru.

Decoding Pega’s AI Ventures

In their core platform, Pega Infinity, the organisation relies heavily on data science, which plays a critical role in analytics, insights generation, natural language processing (NLP), generative AI, and various other applications that drive functionalities such as real-time decision-making and personalised customer communications based on attributes.

Data science also contributes significantly to the development of generative AI models, enhancing the overall intelligence of the platform. Its impact extends beyond the core platform to applications like customer service, one-to-one engagement, decision-making, sales automation, and strategic smart apps for diverse industries.

Pega GenAI provides insights into AI decision-making and streamlines processes, such as automating loan processing. “The benefits of generative AI extend to developers and end-users, improving productivity through query-based interactions, automatic summarisation, and streamlined case lifecycle generation,” Visweswaraiah told AIM. 

End-users also benefit from realistic training scenarios using simulated customer interactions.

Regarding proprietary foundational models, the organisation’s product architecture prioritises openness and flexibility. They support various language models, including those from OpenAI and Google. 

“In upcoming product versions, we are actively working to support and ship local language models to meet specific use case demands, focusing on accuracy, productivity, and performance in response to customer preferences for diverse capabilities,” he added. 

Interview Process

The company follows a global hybrid working model, encouraging collaboration in the office while providing flexibility, with about 60% of the workforce attending the office around three days a week. This approach aims to attract talent globally, fostering a vibrant culture and hybrid working environment.

In upskilling employees, technical competencies are crucial, and the company emphasises learning through its Pega Academy, offering online self-study training, live instructor-led courses, and online mentoring. Skill gaps are regularly assessed during performance reviews, providing learning opportunities through gateways and supporting external courses with an educational reimbursement policy.

“For data science roles, we focus on the candidate’s ability to learn rather than specific data science skills,” Mathur told AIM. The company looks for individuals capable of extracting insights from data, making informed decisions, and building models for application in various use cases.

Mathur further shared that the company emphasises the importance of understanding its problem-solving approach and creating deterministic models that consistently provide performant and real-world solutions. It encourages candidates to think from the customer’s perspective and avoid getting lost in vast amounts of data, highlighting the significance of models producing consistent and reliable answers.

Work Culture

The company emphasises diversity and inclusivity, fostering a culture centred on innovation and collaboration. It has been ranked as the best workplace for women by Avatar for five consecutive years. Pega values individuals who think independently, challenge norms and question the status quo to seek better solutions.

The company encourages leadership and curiosity in approaching tasks, promoting an environment where employees are empowered to innovate. Compared to competitors, Pega’s work culture stands out due to the unique problems it addresses and its distinctive approach.

Understanding the product architecture is crucial for employees, given the nature of the challenges they tackle. Pega’s ability to integrate technology into the platform is a significant differentiator, enhancing its capability to address complex issues. 

“With a focus on adapting to market changes, our mantra of being “built for change” reflects our commitment to staying dynamic and responsive to evolving needs,” concluded Mathur.  

So, if you want to join the dynamic community of Pega, check out the careers page here. 

The post Data Science Hiring and Interview Process at Pegasystems appeared first on Analytics India Magazine.

Headquartered in Mumbai, India, WNS is a prominent global Business Process Management (BPM) and IT consulting company with 67 delivery centers and over 59,000 employees worldwide. 

Combining extensive industry knowledge with technology, analytics, and process expertise, the company collaborates with clients across 10 industries to co-create digital-led transformational solutions. WNS is renowned for its strategic partnerships, delivering innovative practices and industry-specific technology and analytics-enabled solutions. The company’s services cover diverse sectors, characterised by a structured yet flexible approach, deep industry expertise, and a client-centric partnership model.

WNS Triange, the AI, analytics, data and research business unit, has successfully harnessed the power of data science to develop robust solutions that effectively address a myriad of business challenges faced by its clients. 

Among these solutions are sophisticated applications such as an advanced claims processing system, a finely tuned inventory optimisation mechanism, and the implementation of a retail hyper-personalisation strategy.

Consisting of over 6,500 experts, WNS Triange serves as a partner for 200 global clients in more than 10 industries. 

“The team is organised into three pillars: Triange Consult focuses on consulting and co-creating strategies for data, analytics, and AI; Triange NxT adopts an AI-led platform approach for scalable business value; and Triange CoE executes industry-specific analytics programs, transforming the value chain through domain expertise and strategic engagement models,”  Akhilesh Ayer, EVP & Global Business Unit Head – WNS Triange, told AIM in an exclusive interaction last week. 

WNS’s AI & Analytics Play

The data science workflow at WNS Triange follows a meticulously structured process that guides the team through various stages, including problem outlining, data collection, Exploratory Data Analysis (EDA), cleaning, pre-processing, feature engineering, model selection, training, evaluation, deployment, and continuous improvement. A pivotal element of this methodology is the proprietary AI-led platform, Triange NxT, equipped with Gen AI capabilities. This platform serves as a hub for domain and industry-specific models, expediting the delivery of impactful insights for clients.

“When it comes to claims processing, we deploy predictive analytics to conduct a thorough examination of data sourced from the First Notice of Loss (FNOL) and handler notes,” said Ayer. This approach allows for the evaluation of total loss probability, early settlement possibilities, and subrogation/recovery potential. 

Simultaneously, its Marketing Mix Modeling (MMM) is employed to optimise resource allocation by quantifying the impact of marketing efforts on key performance indicators. Furthermore, the application of advanced analytics techniques aids in the detection of suspicious patterns in insurance claims for risk and fraud detection. 

Ayer shared that the team also actively leverages generative AI across diverse sectors. In the insurance domain, it is employed to streamline claims subrogation by efficiently processing unstructured data, minimising bias, and expediting insights for recovery. 

Similarly, in healthcare, it empowers Medical Science Liaisons (MSLs) by summarising documents and integrating engagement data for more impactful sales pitches. Generative AI’s versatility is further demonstrated in customer service interactions, where it adeptly handles natural language queries, ensuring quicker responses and retrieval efficiency.

The combination of LLM foundation models from hyperscalers like AWS with WNS Triange’s proprietary ML models enables the delivery of tailored solutions that cater to various functional domains and industries. Where necessary, WNS Triange employs its AI, ML and domain capability to fine-tune existing foundation models for specific results, ensuring a nuanced and effective approach to problem-solving.

Tech Stack

In its AI model development, the team utilises vector databases and deep learning libraries such as Keras, PyTorch, and TensorFlow. Knowledge graphs are integrated, and MLOps and XAI frameworks are implemented for enterprise-grade solutions. 

“Our tech stack includes Python, R, Spark, Azure, machine learning libraries, AWS, GCP, and GIT, reflecting our commitment to using diverse tools and platforms based on solution requirements and client preferences,” said Ayer. 

Even when it comes to using transformer technology, particularly language models like Google’s BERT for tasks such as sentiment analytics and entity extraction, its current approach involves a variety of language models, including GPT variants (davinci-003, davinci-codex, text-embedding-ada-002), T5, BART, LLaMA, and Stable Diffusion. 

“We adopt a hybrid model approach, integrating Large Language Models (LLMs) from major hyperscalers like OpenAI, Titan, PaLM2, and LLaMA2, enhancing both operational efficiency and functionality,” he commented. 

Hiring Process

WNS Triange recruits data science talent from leading engineering colleges, initiating the process with a written test evaluating applied mathematics, statistics, logical reasoning, and programming skills. Subsequent stages include a coding assessment, a data science case study, and final interviews with key stakeholders.

“Joining our data science team offers candidates a dynamic and challenging environment with ample opportunities for skill development. And while engaging in diverse projects across various industries, individuals can expect exposure to both structured and unstructured data,” said Ayer. 

The company fosters a collaborative atmosphere, allowing professionals to work alongside colleagues with diverse backgrounds and expertise. Emphasis is placed on leveraging cutting-edge technologies and providing hands-on experience with state-of-the-art tools and frameworks in data science. 

WNS Triange values participation in impactful projects contributing to the company’s success, offering access to mentorship programs and support from experienced team members, ensuring a positive and productive work experience.

Mistakes to Avoid

Candidates are encouraged to not only showcase technical prowess but also articulate the business impact of their work, demonstrating its real-world relevance and contribution to business goals.

Ayer emphasised, “Successful data scientists must not only be technically adept but also skilled storytellers to present their findings in a compelling manner, as overlooking this aspect can lead to less engaging presentations of their work”

He added that candidates sometimes focus solely on technical details without articulating the business impact of their work, missing the opportunity to demonstrate how their analyses and models solve real-world problems and contribute to business goals.

Work Culture

Recognised by TIME MAGAZINE for being one of the best companies to work in,  WNS has built a work culture centered on co-creation, innovation, and a people-centric approach, emphasising diversity, equity, and inclusivity, prioritising a respectful workplace culture and extending its commitment to community care through targeted programs by the WNS Cares Foundation. 

“Our focus on ethics, integrity, and compliance ensures a safe ecosystem for all stakeholders, delivering value to clients through comprehensive business transformation,” said Ayer. 

In terms of employee perks, it offers various services and benefits, including transportation, cafeterias, medical and recreational facilities, flexibility in work hours, health insurance, and parental leave. 

“Differentiating ourselves in the data science space, we cultivate a work ecosystem that fosters innovation, continuous learning, and belongingness for the data science team. Our initiatives include engagement tools, industry-specific training programs, customised technology-driven solutions, and a learning experience platform hosting a wealth of content for self-paced learning,” he added. 

Why Should You Join WNS?

“At WNS, we believe in the transformative power of data, where individuals play a key role in shaping our organisation by directly influencing business strategy and decision-making. Recognising the significant impact of data science, we invite individuals to join our collaborative and diverse team that encourages creativity and values innovative ideas. In this dynamic environment, we prioritise knowledge sharing, continuous learning, and professional growth,” concluded Ayer. 

Find more information about job opportunities at WNS here.

The post Data Science Hiring and Interview Process at WNS appeared first on Analytics India Magazine.

Founded in 2011, New York-based IT services and consulting firm Marlabs helps companies of various sizes to undergo AI-powered digital transformation. It provides a wide range of services, including strategic planning, creating rapid prototypes in specialised labs, and applying agile engineering techniques to develop and expand digital solutions, cloud-based applications and AI-driven platforms.

Marlabs’s data science team addresses a range of industry challenges, emphasising tasks like extracting insights from extensive datasets and employing pattern recognition, prediction, forecasting, recommendation, optimisation, and classification.

Exploring Generative AI at Marlabs

“In operationalising AI/ML, we have tackled diverse projects, such as demand forecasting, inventory optimisation, point of sale data linkage, admissions candidate evaluation, real-time anomaly detection, and clinical trial report anomaly detection,” Sriraman Raghunathan, digital innovation and strategy principal, Marlabs, told AIM in an exclusive interaction. 

The team is also exploring generative AI applications, particularly in knowledge base extraction and summarisation across domains like IT service desk ticket resolution, sustainability finance, medical devices service management, and rare disease education.

However, it is not developing foundational models as of now due to substantial capital requirements. “Instead, we are focussing on the value chain beyond foundational models, offering tools and practices for deploying such models within organisation boundaries, tailored for specific domains,” he added. 

Marlabs employs a variety of tools and frameworks depending on project specifics, utilising R and Python for development, Tableau, Power BI, QlikView for data exploration and visualisation, and PyTorch, TensorFlow, Cloud-Native tools/platforms, and Jupyter Notebooks for AI/ML model development.

The team leverages Transformer models like GPT-3, especially in NLP use cases, implementing them in TensorFlow, and PyTorch, and utilising pre-trained models from Hugging Face Transformers Library. For generative AI, their toolkit includes LangChain, Llama Index; OpenAI, Cohere, PaLM2, Dolly; Chroma, and Atlas.

Hiring Process

The hiring process for data science roles at the organisation emphasises a blend of technical knowledge, practical application, and relevant experience. The initial steps involve a clear definition of the role and its requirements, followed by the creation of a detailed job description. 

The interview process comprises technical assessments, video interviews with AI/ML experts, and HR interviews. Technical assessments evaluate coding skills, data analysis, and problem-solving abilities. 

Video interviews focus on the candidate’s depth of knowledge, practical application, and communication skills, often including a discussion of a relevant case study or project. HR interviews center around cultural fit, interpersonal skills, collaboration, and the candidate’s approach to handling challenges. 

Expectations

“Upon joining the data science team, candidates can anticipate a thorough onboarding process tailored to their specific team, providing access to essential tools, resources, and training for a smooth transition,” commented Raghunathan. 

The company’s AI/ML projects involve cutting-edge technologies, exposing candidates to dynamic customer use cases spanning natural language processing, computer vision, recommendation systems, and predictive analytics. The work environment is agile and fast-paced. The company places a strong emphasis on team collaboration and effective communication, given the collaborative nature of data science and AI/ML projects. 

In this rapidly evolving field, the company expects new hires to demonstrate continuous learning, tackle complex technical and functional challenges, operate with high levels of abstraction, and exhibit creative and innovative thinking.

Mistakes to Avoid

“The most prevalent error observed in candidates during data science role interviews is a lack of clear communication,” he added.

The ability to effectively communicate insights to non-technical stakeholders is crucial in the AI/ML space, and this skill is frequently overlooked. 

Another common mistake is a failure to comprehend and articulate the business context and domain knowledge of the problem, which is essential in AI/ML applications with significant business impact.

Work Culture

“We are recognised for our value-based culture focused on outcomes, emphasising a flat organizational structure to spur innovation and personal growth. Key values such as respect, transparency, trust, and a commitment to continuous learning are central to their ethos, all aimed at exceeding customer expectations,” he said.

The company’s robust learning and development program has prepared over 150 young managers for leadership roles, with a strong emphasis on AI and technology for organisational insights and sentiment analysis.

The company offers a comprehensive benefits package, including versatile insurance plans, performance incentives, and access to extensive learning resources like Courseware and Udemy, supporting a hybrid work model. Additionally, they provide mental health support and reward long-term employees based on tenure. 

Raghunathan further explained that in the data science team, Marlabs stands out for its innovative and collaborative environment, encouraging creativity and continuous learning. “This distinctive culture and investment in employee growth make us a leader in data science, differentiating it from competitors in the tech industry,” he added. 

Why Should You Join Marlabs?

“Join Marlabs for a dynamic opportunity to work with a passionate team, using data to drive meaningful change. In this collaborative setting, data scientists work with brilliant colleagues across various industries, including healthcare, finance, and retail. You’ll tackle complex issues, contributing to significant business transformations. Marlabs supports your career with essential tools, resources, training, competitive compensation, benefits, and opportunities for professional growth and development,” concluded Raghunathan.  

The post Data Science Hiring and Interview Process at Marlabs appeared first on Analytics India Magazine.

Dream Sports, the parent company of Dream11, is a prominent sports tech unicorn in India that houses a bunch of brands like Dream11, FanCode, and more, making it the Willy Wonka’s Chocolate Factory of sports engagement. Founded in 2008 by Harsh Jain and Bhavit Sheth, Dream Sports is headquartered exclusively in Mumbai, affectionately dubbed “The Stadium” by its team.

At the forefront, its flagship fantasy sports platform, Dream11, accommodates a massive user base of over 190 million individuals. This platform offers users the exciting opportunity to participate in fantasy versions of cricket, hockey, football, kabaddi, handball, basketball, volleyball, rugby, futsal, American football, and baseball.

And for the seamless operation of Dream Sports, data science has become a cornerstone in tackling fundamental challenges for platforms like Dream11, where user experience and engagement play pivotal roles. 

AIM got in touch with Amit Sharma, chief technology officer at Dream Sports (Dream11) to know more about their AI, ML operations, hiring strategy, work culture and more. Since 2016, Sharma has led the creation of the sports tech platform. Earlier, he spent over a decade developing complex distributed systems for major companies like Yahoo! and Netflix in California. 

Dream Sports is currently looking out for a VP of Data Science in Mumbai to lead data science roadmap development, drive experimentation, propose ML solutions, mentor the team, and ensure goal alignment. Required skills include programming languages, data visualisation, machine learning, and strong interpersonal skills. Familiarity with technologies like Cloudfront, API Gateway, Python, MySQL, Kafka, Spark, and Redshift is essential. 

Inside Dream11’s AI & Analytics Play

Aligned with its mission of enhancing the sports experience, Dream Sports operates akin to a “high-performing sports team” composed of “Coaches” (CXOs) and “Captains” (team leaders) who guide over 1000 “Sportans” (employees). That includes a skilled roster of engineers, business and data analysts, applied scientists and machine learning experts. 

One of the primary hurdles that this team has successfully tackled through data science is the issue of personalisation, discovery, and fraud detection within its application. When it comes to user interaction, the app encompasses a multitude of features, like matches and contests, resulting in a notable mental demand. These elements are subject to change, creating challenges for users to fully grasp and decide on the most suitable choices. Dream11 was an early adopter of data science in its product development journey, even during when these technologies were relatively nascent within the tech ecosystem. 

“In order to improve user experience and engagement, we have deployed over 100 AI and ML models to enable the contextual discovery of relevant features. We have personalised multiple user journeys throughout the app by analysing user cohorts and behaviours,” said Sharma. 

Underpinning these offerings are sophisticated ML systems that process an extensive array of over 1000 features across numerous users. Appropriate models and data drifts have also been implemented to ensure the smooth functioning of daily operations. The team of data scientists has also developed robust systems for detecting fraud by incorporating knowledge graphs, extensive searches for similarities, and algorithms for recognising patterns. They consistently carry out tests and refine methods for personalisation. They use A/B testing to compare various user experiences and gauge their effects on user engagement.

Data scientists at Dream11 continually experiment with various personalisation strategies, employing A/B testing methodologies to compare distinct user experiences. This iterative approach helps in identifying the most effective strategies while quantifying their impact on user engagement.

Tech Stack

Dream Sports’ technological infrastructure is a blend of in-house tools and third-party solutions. 

“While our tech infrastructure is a combination of in-house tools and third-party solutions, we strongly believe in developing in-house solutions to minimise costs, strengthen data privacy, and control the scalability of services without compromising the architecture,” said Sharma. 

One of their in-house frameworks, known as FENCE (Fairplay Ensuring Network Chain Entity), is employed to identify and address Fairplay violations, ensuring fair competition for users.

Their primary distributed ML systems rely on Spark and Ray. Transformers are utilised for various sequential learning tasks and have demonstrated superior performance compared to other deep learning models on their datasets, which are awaiting large-scale implementation. “We are exploring applications of LLMs in the context of the Sports ecosystem and testing internal prototypes,” Sharma commented.

For forecasting and classical machine learning applications, they rely on a range of resources, including Scikit Learn, XGboost, Prophet, and Scipy. Additionally, for deep learning-based machine learning tasks, the team leverages the capabilities of Pytorch and Tensorflow, harnessing their power to create robust and advanced models.

On the front of app development, Dream11 became one of the few tech companies to fully migrate their platform to React Native, a UI software framework. Despite industry scepticism regarding the feasibility of complete React Native adoption due to its historically low success rates, Dream11 navigated and overcame the associated challenges to make this happen. 

Interview Process

Dream11 places a strong emphasis on valuing skills when hiring, seeking top talent aligned with their goal of enhancing the sports experience, encapsulated by the acronym “DOPUT”: Data-Driven, Ownership, Performance, UserFirst, and Transparency. 

When hiring data science professionals, the organisation prioritises cultural fit initially. Upon meeting this criterion, candidates undergo a customised hiring process that varies by role. For most data science applicants, this process includes an aptitude test, followed by progressive technical interviews covering areas such as R programming, ML, and practical mock projects. Domain-specific interviews led by team leaders provide a thorough evaluation of the candidate’s preferences and skills.

One of the common mistakes that candidates make while interviewing is sometimes they miss out on the basic foundations of ML, stats or experimentation which are extremely valued at the organisation. 

“While building models is easy, making them useful is tougher. That’s where hands-on implementations become a force multiplier,” said the CTO. Prospective team members can expect a supportive work environment with access to extensive qualitative data, challenging machine learning tasks, advanced infrastructure, a motivated team, and the chance to contribute at a large scale.

Work Culture

Driven by its culture, the company fosters an open and transparent atmosphere. Certified as a Great Place to Work, Dream Sports adopts a hyper-experimentation approach known as “HEAL – Hypothesis, Experiment, Analysis, and Learning” allowing employees to experiment, embrace failure, swiftly learn, and create personalised user features.

Employees enjoy a range of special perks and benefits like ‘Learning Wallet‘, unlimited leaves, ESOP, insurance, mental wellness initiatives and more. The Learning Wallet supports diverse learning ambitions, allowing individuals to explore areas such as design or coding regardless of their primary expertise. The unlimited leave policy promotes a healthier work-life balance, including the ‘Unplugged’ feature—a unique seven-day work-free vacation opportunity. Additionally, employees enjoy fully-paid access to sports events, matches, and tournaments.

“Most importantly, besides several industry-first benefits, we offer access to the latest tech stack and prioritise building a thriving culture through various engagement activities,” concluded Sharma.

Check out their careers page here. 

Read more: Data Science Hiring Process at Naukri.com

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Halfway into 2023, and over two lakh employees world over have already been laid off. Amidst this crisis crippling the job market, AI-powered HRTech platform Instahyre is making sure to provide the best of opportunities.

In solving the pain of millions of job seekers, Instahyre’s data science team has successfully tackled one of their primary challenges by optimising the job-matching process through ‘Instamatch’ their proprietary recommendation system. It improves the efficiency and effectiveness of the job search experience for both job seekers and employers.

“Instamatch has changed how companies approach hiring, changing the modus operandi from mass emails, keyword search, and unanswered phone calls to a holistic data-driven, tech-based candidate personality and company DNA mapping, which has taken candidate experience and hiring conversions to a whole new level, reducing the time and cost to hire drastically,” said Sarbojit Mallick, cofounder of Instahyre, in an exclusive conversation with Analytics India Magazine.   

Founded in the year 2017 by Aditya Rajgarhia and Mallick, Instahyre’s adept use of AI, ML and data science in its operations has resulted in an optimised recruitment platform that provides personalised job matches, streamlined candidate evaluation, and improved efficiency for recruiters and candidates alike.     

The company boasts about a 70% reduction in time to hire and cutting costs by thrice compared to traditional methods. With over 10,000 companies benefiting from their services and a staggering 40 million candidates on their platform, Instahyre has earned the trust of major industry players such as Amazon, Google, PayPal, Salesforce, Walmart, Oracle, Razorpay, Paytm, PhonePe, JP Morgan, Adobe, and Myntra.    

Inside the AI and ML Operations of Instahyre

In its operations, Instahyre effectively implements AI and ML to harness the power of data science and derive valuable insights. 

A prominent area where data science is applied at Instahyre is in candidate-company matching. Using InstaMatch, the data science team ensures that job seekers are matched with companies based on a comprehensive set of factors, including skills, experience, and individual preferences. This results in a more precise and personalised job-matching experience for users.

Additionally, Instahyre employs natural language processing (NLP) and machine learning algorithms to parse and analyse resumes and allows the platform to extract relevant information from resumes, allowing for a streamlined and time-saving candidate evaluation process for both job seekers and employers. The platform has been utilising generative AI since its inception six years ago. 

Moreover, Instahyre assists recruiters with tools like Instahyre Talent Insights, which provides a global overview of the talent pool for each job post. The AI-driven approach automates various aspects of the hiring process, including candidate sourcing, shortlisting, and scheduling interviews. It also conducts deliberate screening and assessment, leading to effective candidate evaluation and offer rollouts for chosen individuals.

Tech Stack Employed

Instahyre uses various technology capabilities, including an Application Tracking System (ATS) Integration. It is integrated with widely-used applicant tracking systems used by employers and recruiters. This facilitates a smooth transfer of candidate data, updates application statuses, and streamlines the entire hiring process for the company.

Furthermore, the data science team at the company employs a range of tools, applications, and frameworks to tackle challenges and make informed decisions. These resources encompass MySQL, Python, Java, NLP, and other relevant technologies. By combining these tools, they aim to excel in data-driven endeavours and maintain a dynamic work environment.

Interview Process

Instahyre seeks potential candidates with specific expertise in different domains. For the ML Engineer role, they require proficiency in Python, NLP, and other deep learning concepts. For the Data Engineer position, the desired skills encompass Python, Java, and Scala, as well as experience with technologies like Hadoop, Spark, Kafka, MySQL, MongoDB, Cassandra, AWS, and Azure.

Their data science hiring process involves multiple steps to find the right candidates that begins with a thorough review of resumes, focusing on educational background, work experience, and essential skills in mathematics, statistics, programming, and machine learning. Shortlisted candidates then undergo a technical assessment, evaluating their abilities in data analysis, programming languages, statistical modeling, and problem-solving.

Successful candidates proceed to a technical interview, where their expertise and problem-solving capabilities are extensively examined. Lastly, behavioural interviews assess candidates’ communication skills, problem-solving approach, and cultural fit within the collaborative team environment.

Expectations

Once selected, candidates joining the data science team at Instahyre can expect a dynamic and intellectually stimulating environment. They will have the opportunity to work on cutting-edge technologies, solve challenging problems, and collaborate with a highly skilled and diverse team. The company provides access to the latest tools and resources to support their work and encourages continuous learning and professional development.

“In return, Instahyre expects candidates to have a solid foundation in data science, including a strong understanding of statistics, mathematics, and machine learning algorithms, added Mallick.

Excellent programming skills, strong analytical thinking, problem-solving abilities, and effective communication of complex ideas are also highly valued qualities expected from candidates joining the data science team.

Mistakes to Avoid

When interviewing for a data science job at Instahyre, candidates should avoid some common mistakes that can hamper their chances. One mistake is not showing practical experience and how their work has made a real-world impact. Candidates should share specific project examples, challenges they faced, and the outcomes they achieved.

Another mistake is not preparing well for technical questions or lacking a good understanding of core data science principles. Being well-prepared and showing mastery of important concepts is important to impress the interviewers. Candidates should highlight their practical experience with different datasets and explain how their work has made a difference. Instahyre values innovation, creativity, and a growth mindset, so applicants should try to embody these qualities

Work Culture

“Our work culture is positive and the best part is there is no micromanagement. Employees are trusted and given autonomy to handle their tasks, leading to increased productivity and accountability,” added Mollick. The company follows a remote work policy, allowing employees to work from their preferred locations, allowing Instahyre to tap into talent from diverse geographic areas.

“What sets Instahyre apart from its competitors, especially in terms of working with the data science team, is its collaborative and cross-functional approach, he added.

The company encourages collaboration between data science and other teams, fostering diverse perspectives and knowledge-sharing to solve complex problems. It also places a strong emphasis on innovation and continuous learning, offering opportunities for professional development and research activities. 

So if you want to work on impactful projects that directly benefit users and the recruitment industry as a whole but also grow professionally and personally, Instahyre is the place for you. Check out their careers page now.

Read more: Data Science Hiring Process at Naukri.com

The post Data Science Hiring and Interview Process at Instahyre appeared first on Analytics India Magazine.

Founded in 1997 by Sanjeev Bikhchandani, the founder of InfoEdge and Ashoka University, Naukri.com is India’s largest platform for white collar job seekers as well as recruiters.

To cater to these distinct user groups, Naukri developed state-of-the-art AI-powered vertical search and recommendation engines in the country. Comprising a talented group of around 60 data scientists, 15 ML engineers, and 20 analytics professionals, the team operates under specialised units like Search, Recommendation, Language Model (Taxonomy), Pricing, User Acquisition, Content Generation, Job-CV Matching, and Information Extraction. Each unit is expertly led by a lead data scientist or an AVP of data science, guaranteeing strong leadership and focused proficiency.

“We take pride in having solved the challenges of ‘job seeker discovery’ and ‘job discovery’ for each group respectively,” Jatin Thukral, executive vice president & head – data science, Naukri.com, told AIM. 

Naukri has carved a unique path in the world of B2B data science and AI with its exceptional AI and analytics team. They effectively solve business problems by collaborating with product managers and and engineering teams to make it possible. The company has been investing in AI R&D since 2011, amassing over a decade of invaluable experience as a trailblazer in B2B data science and AI. 

Introduced in 2022, Naukri’s ResDex is India’s largest resume database built by leveraging data science vigorously. Recruiters use this platform to handle over one million job mandates from a vast pool of eight crore applications available on Naukri. 

The company is currently hiring for various data scientist roles in senior, lead, AVP, and VP positions.

The experience levels required for these roles are as follows: Data Scientist (0-4 years), Senior Data Scientist (4-8 years), Lead Data Scientist (6-10 years), AVP Data Science (8-12 years), VP Data Science (10-14 years), and SVP Data Science (10-16 years).

AI & Analytics Play at Naukri.com

Naukri.com’s data science efforts cater to both B2B and B2C needs. On the B2B front, the focus is on optimising recruiter efficiency through an advanced CV search engine and precise candidate recommendations based on their unique journey. For B2C users, the company provides personalised job recommendations, salary-related insights, and an easy-to-navigate job search engine.

“Our approach to AI model development is unique because of its flexibility, as it avoids restricting itself to specific AI architectures. Instead, the team relies on a combination of various models to effectively address real-world challenges, fostering innovation and adaptability,” added Thukral.

One of the prime examples of their AI implementation is harnessing the power of AI to automatically generate job requirements tailored to the recruiters’ search actions and patterns. This was made possible using various tiers of hierarchical agglomerative clustering models where the first stack is based on single linkages and the subsequent ones are based on multiple linkages. 

With a dedicated ecosystem, the company focuses on developing, deploying, and monitoring AI and ML-based engines to address various business challenges and deliver superior client solutions. 

To achieve this, a diverse range of AI models and techniques are employed, including deep learning models like LSTMs, Bi-Directional LSTMs, BERT, CNNs, Transformers, Attention Mechanisms, and GANs. The most commonly used classical machine learning and statistical models are Random Forests, XGBoost, Hierarchical and Divisive Agglomerative Clusterings, and Logistic Classifiers. 

Through training these open-source models on proprietary data and combining them into AI architectures, the organisation optimises problem-solving capabilities and ensures effective deployment into products.

Their job recommendation engine, RecoClus leverages over 200 multiple models to provide personalised job recommendations. It automatically extracts information from candidates’ resumes and matches them with the most suitable job openings, learns candidates’ preferences based on their previous job applications, encompassing various job categories like technical, HR, finance. It then recommends jobs based on recent search behaviour and optimises job notifications, allowing applicants to apply.

The ResDex Enterprise platform analyses recruiters’ past activity to personalise search results to deliver the most relevant talent options. For example, if a recruiter from Amazon is acting heavily on job seekers from Flipkart, the model will automatically learn this behavior and enhance visibility of job seekers from Flipkart in the future searches of the recruiter.

Hiring Process

The hiring process for data science roles is rigorous and consists of five rounds of interviews to assess candidates’ understanding of statistics, linear algebra, classical machine learning, and deep learning. Candidates are also asked to solve real-world programming case studies live with a recruiter. The next round involves a senior data science leader evaluating technical skills and data science temperament. The final round is an HR assessment for personality and cultural fit. 

Candidates must have a degree (BTech/MTech/MSc/PhD) from prestigious institutions such as IIT, IISc, ISI, IIIT in Maths or Engineering. Prior experience in a top 10 AI team is advantageous.

Expectations

The ideal candidate is expected to have skills including Mathematics, Statistics (Probability, Hypothesis Testing, etc.), Linear Algebra, Classical ML, Algorithms (RF, Boosting, etc.), Testing (Performance Metrics), Loss functions, Deep Learning (LSTMs, CNNs, etc.), Data Processing (NLP, Text Mining), DL Libraries (TensorFlow, PyTorch), LLM Frameworks (Vicuna, MPT-7B), and Programming Languages (Python with PySpark interface), Databases (SQL, MongoDB, HDFS).

Joining Naukri’s data science team brings a fast-paced research environment, autonomy for independent projects, collaboration with smart colleagues, meritocracy, data-driven decision-making, and ample learning opportunities.

Work Culture

According to Thukral, the team at Naukri is young, dynamic, and collegial, and the work culture fast-paced. When it comes to data science, it holds a crucial role as the core of the Naukri business and innovations. “This makes the job of a data scientist at Naukri very exciting, albeit challenging,” he added. 

At all levels, data scientists fully embrace the responsibility of overseeing one or more projects from start to finish, having complete ownership of the final business outcomes. They collaborate closely with diverse stakeholders, including product managers, ML engineers, technologists, design teams, and more. Naukri aims to maintain a strong balance sheet and follows the highest level of professional ethics, along with conservative cash management. 

Additionally, data science and AI play a crucial role in powering Naukri’s top-selling products, distinguishing it as one of the consistently lucrative internet enterprises in India. With Naukri.com, you can focus on building a long-term career with the company, as it values stable growth over forced job hopping. Furthermore, Naukri benefits from the guidance of some of the best thought leaders in the internet industry, providing employees with an enriching and supportive environment for professional development. 

Overall, Naukri.com presents an attractive opportunity for those seeking a rewarding and impactful career in the field of data science and AI, backed by a strong foundation and a thriving work culture.

Check out their openings now. 

The post Data Science Hiring and Interview Process at Naukri.com appeared first on Analytics India Magazine.

American retail giant Lowe’s operates a chain of over 1700 stores, .com, and app in the US and is the second-largest hardware chain in the world, trailing behind Home Depot.

With its Global Capability Centre in Bengaluru, Lowe’s India boasts a robust team of over 4,000 members who deliver exceptional service to 17 million plus customers. To make this possible, data plays an important role, particularly in the retail industry, where it is vital for decision-making processes such as search, product recommendations, inventory management, supply chain operations, and demand forecasting.

And the data science team at Lowe’s India are at the forefront of this.  

The Data, Analytics, & Computational Intelligence (DACI) team at Lowe’s supports the company’s global business endeavours by delivering prompt, relevant, and profoundly actionable data, analytics, and cutting-edge analytic services and solutions.

“Some of the key areas of innovation by the DACI team include computer vision applications, homegrown machine learning platform and many operational and enterprise analytics insights,” said Amit Kapur, Vice President, Data & ML Platforms and Data Governance in an exclusive interaction with AIM. 

The DACI team is divided into ‘products’ and ‘platforms’ teams, spread across the US and India. The DACI platforms team is heavily led from India comprising over 300 members including all levels of data engineers, software engineers, data analysts, and data scientists.

For instance, the merchandising teams have harnessed data insights provided by the DACI team to elevate and optimise their sourcing strategies. Moreover, the Stores teams at Lowe’s are leveraging the computer vision platform developed by the data science team, bolstering customer service and boosting productivity. Additionally, the marketing teams have devised customer lifetime value models, enabling them to gain deeper insights into customers and target them more effectively. The finance team employs machine learning models to forecast tax codes, thereby enhancing accuracy and efficiency in tax-related operations.

Currently, DACI has over ten open positions for data scientists in Bangalore. These roles require a range of experience levels, from four to fifteen years.

Interview Process

The data science hiring process involves resume screening to assess qualifications, followed by a technical assessment to evaluate proficiency and problem-solving skills. A deep-tech interview focuses on algorithmic problem-solving, communication, and analytical thinking. 

The final round determines a candidate’s fit by assessing their ability to communicate clearly, solve problems efficiently, and demonstrate the business impact of their work. Aligning contributions with strategic goals provides an advantage, especially at senior levels.

Some common key result areas include technical proficiency, machine learning algorithms, data visualisation, statistical and mathematical aptitude, and relevant experience in the retail industry. 

“Given that Lowe’s is a home improvement retailer, candidates with expertise in data analysis and exploration, particularly in the retail sector, have a competitive edge,” added Kapur. 

Expectations

Meanwhile, the data science team at Lowe’s requires a range of skills including proficiency in machine learning algorithms, statistical analysis, problem-solving approaches, strong Python coding skills, and data visualisation. 

In terms of technology capabilities, the team utilises ML/AI, deep learning, clustering, time series forecasting, Python, SQL, linear regression, and statistical modeling. They employ various tools, applications, and frameworks such as Python, ETL (Extract, Transform, Load), Scikit-learn for machine learning algorithms, ML Flow, KubeFlow, Feast, and Explainable AI to support their work.

Additionally, Lowe’s values an innovation mindset, encouraging creative thinking and continuous improvement for the benefit of our customers and associates. 

Kapur points out that one of the common mistakes often make the mistake of overly emphasising theoretical knowledge during interviews instead of demonstrating the practical application of skills to real-world problems. Candidates should highlight past projects, explaining their approach, techniques used, and achieved results. 

“Especially at senior levels, it is important to quantify the business impact of their work. Candidates who can articulate how their work will contribute to the company’s strategic goals and can communicate their findings in a clear and concise manner will have a significant advantage during the interview process,” Kapur commented. 

Work Culture

Lowe’s takes pride in its unique work culture, where associates are driven by core values like customer focus, courage, action, results, and continuous learning. Trust, respect, empathy, and agility form the foundation of the organisation. 

“Our people policies and benefits are inclusive and are designed to ensure we have a diverse talent pool. Currently, our gender ratio is at 33% – much ahead of the industry” said Kapur. 

Special perks at Lowe’s include ESOP, insurance, workcations, wellness programs, parental support, and skill development training, among others. 

“If you’re driven, eager to learn, and can solve complex problems at scale, Lowe’s offers the perfect opportunity to shape the future of omnichannel retail,” concluded Kapur. 

Click here to check out their careers page. 

Read more: Data Science Hiring Process at Mastercard

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Since its formation in 1966, financial services giant Mastercard’s goal has been to connect and power an inclusive, digital economy by making transactions safe, simple, smart and accessible. Headquartered in Purchase, NY, Mastercard offers an extensive array of cutting-edge financial services. It was formulated by a coalition of various financial institutions and localised bankcard unions to counter BankAmericard, introduced by Bank of America, which subsequently morphed into Visa, its primary rival even to this day.

“We are dedicated to enhancing the security of the payments industry by using data science,” said Rajesh Chopra, senior vice president, data & services, South Asia, Mastercard, told AIM in an exclusive interview. 

Mastercard’s expertise in this area provides great value for its stakeholders. The global payments company is also introducing an innovative AI-based solution that enhances banks’ ability to detect potential fraudulent money transfers by their customers. Several prominent UK banks, including Lloyds Banking Group Plc, Natwest Group Plc, and Bank of Scotland Plc, have partnered with Mastercard to utilise the Consumer Fraud Risk system.

Inside Mastercard’s AI & Analytics Play

Mastercard leverages AI to prevent cyberattacks and fraudulent activities, saving more than $30 billion in the last two years alone. Additionally, Mastercard employs Decision Intelligence to address real-time business requirements while avoiding disruptions. 

By analysing multiple data points and implementing advanced modelling techniques,  transaction decision score is generated, offering valuable insights to card issuers. By leveraging this score, issuers can optimise authorisation decisions, approving legitimate transactions while maintaining robust security measures. 

Additionally, Mastercard employs AI to support banking partners in effectively managing credit risk. This ensures that customers are provided with appropriate credit amounts, enhancing the overall customer experience.

Mastercard also banks on data analysis expertise to empower decision-making, exemplified by the introduction of Recovery Insights during the COVID-19 pandemic. This comprehensive solution aids businesses and governments in managing health, safety, and economic risks through analytics, experimentation, consulting, and unique data-driven insights. 

The company also prioritises technological advancements, such as implementing robotic process automation to streamline operations and enhance employee experiences. Additionally, Mastercard’s AI-powered global learning platform, Unlocked, provides employees with access to relevant mentorship opportunities and global projects, fostering continuous professional growth.

“At Mastercard, upholding data protection and privacy as fundamental human rights is of paramount importance so we employ a dedicated team of data scientists and AI technologists to ensure consistent adherence to responsible AI development and application,” Chopra emphasised.  With a strong governance process and adherence to best practices, they strive to minimise biases in AI models and data, promoting fair and equitable outcomes.

Hiring Process

Mastercard employs a fair and flexible system to hire talent from various regions. Candidates go through screening based on required skills, followed by technical assessments and interviews to evaluate their behavior, expertise, and domain knowledge. 

Currently, Mastercard is looking to hire skilled analysts, consultants, senior analysts, assistant managing consultants, and leadership positions such as managing consultants and senior managing consultants in advanced analytics. The desired expertise profiles for these roles include credit risk, fraud analytics, and other subject matter expert (SME) domains.

For consultant roles in analytics, a minimum of two years of experience is required. Preferred domain knowledge includes finance and retail analytics, payments, fintech, marketing, and commercialisation of scalable platforms. Senior analytical roles, on the other hand, consider experience ranging between five and seven years.

The key skills Mastercard looks for are passion, analytical excellence, project management, good communication, teamwork, integrity and bringing a diverse perspective. 

On the technical side, when considering potential candidates, proficiency in various tools, applications, and frameworks is important. These include platforms and database environments such as Hadoop, Oracle, and MySQL Server.

Programming skills in Python, Pyspark, R, Rshiny, Spark, Impala, Hive, and SQL are also desired. Knowledge of BI tools like Tableau, Power BI, Alteryx, MSBI stack, Angoss, Think cell, Bitbucket, Adobe Analytics, and Toad is beneficial. Additionally, familiarity with data science techniques like custom analytics using AI/ML, deep learning, statistical modelling, model development, market research, H2O, and Test & Learn is valuable.

In terms of education and certifications, an advanced degree in Economics, Statistics, Mathematics, Finance, or Engineering with a focus on business applications is preferred. Certifications in the mentioned tools and skills are also beneficial. Having knowledge of business analytics, as well as certifications such as PMP and Scrum Master, are considered advantageous for these roles.

“Alongside technical proficiency, we highly value qualities like empathy, a desire to learn, willingness to collaborate, taking responsibility, adaptability, and a commitment to making a positive impact, not just achieving personal success,” said Chopra. 

Work Culture

Mastercard is certified as a ‘Great Place to Work’ and the company considers people as “biggest assets”. With a firm belief in fostering a culture of “decency”, Mastercard recognises that a strong workplace culture not only supports employee growth but also positions itself as a positive contributor to the community. 

Three key areas contribute to cultivating this culture. Firstly, Mastercard places great emphasis on work flexibility, ensuring inclusivity and well-being of its workforce. Flexible time policies, coupled with a supportive culture, values, diversity, inclusion, career opportunities, and rewards, are all factors contributing to this recognition.

Secondly, employee health and well-being are important for Mastercard. Through its “Live Well” global digital program, Mastercard offers online sessions on healthy eating, exercise, meditation, and expert advice, supporting employees in maintaining their mental and physical well-being. Additional support is provided through benefits like childcare, bereavement leave, flexible scheduling, counselling, and fitness facilities.

Lastly, Mastercard invests significantly in upskilling its workforce, with a focus on project-based learning and mentoring. 

“We are driven by a culture of Diversity, Equity, and Inclusion (DEI) that arises from our wider objective of empowering people, preserving the planet, and fostering prosperity. We aim to cultivate a culture that celebrates diversity of thought, background, experiences, and abilities,” Chopra concluded. 

So if you are looking for a change in job role, tap on this link. 

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