Data Science Hiring Process At PhonePe

Data Science Hiring Process At PhonePe

80% of data science is hard manual work of looking at data.

PhonePe is one of the largest fintech players in the country with 304 million users spread across 12,000 towns and 20 million stores. The digital payment company’s  data science team is engaged in fine-tuning marketing campaigns, optimising operating costs, deep personalisation to drive user satisfaction, preventing fraud attempts, safeguarding users and driving revenues.

The head of data science at PhonePe, Kedar Swadi, told Analytics India Magazine that his team has a solid background in algorithms, mathematics and statistics. “It is always easy to learn new techniques once the basics are strong,” he added. 


Further, he said the team members have good programming skills and know-how to efficiently handle a large amount of data and create scalable pipelines. “We believe there is no compression algorithm for experience, and we specifically are staffed with people with deep domain expertise in financial services and, more narrowly, in payments,” said Swadi.

Team structure 

Currently, PhonePe has a 15-person data science team. “The team size grows based on the requirements we get from other teams in the organisation, and our plans for enhancing skills and capabilities in core data science as well as our expertise in the various domains that we work with,” said Swadi. 

PhonePe’s data science team consists of a mix of freshers and experienced professionals. The company follows a flat organisation structure.

“While all members focus on the core technical aspects, they also start contributing in other orthogonal areas such as data-driven innovations for business teams, mentoring junior members, process improvement for more predictable delivery, etc. as they grow,” said Swadi. 

PhonePe’s data science staff work in ‘pods’ (teams) to build long-term and short term solutions. 

Interview process 

Currently, PhonePe has three rounds of interviews: 

  • A coding round where they test the candidate’s ability to use tools and libraries to analyse, interpret and visualise data
  • A Q&A round, where they focus purely on the candidate’s understanding of core technical aspects (maths, statistics and algorithms) 
  • An online (or offline) problem-solving session, where they give a problem statement and test the candidate’s ability to structure it into a crisp business problem, 

The last round of the interview is of paramount importance. Here, the interviewer looks at the candidate’s ability to distil the problem statement into data science tasks, explain the data required to solve the problem, describe and justify the techniques used to solve it, and then discuss how it can be presented to business or domain specialists.

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