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What Is The Hiring Process For Data Scientists At HDFC Bank

What Is The Hiring Process For Data Scientists At HDFC Bank

Srishti Deoras

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Data science function in banks has a crucial role to play in various use cases such as fraud detection, managing customer data, risk modelling, personalised marketing, among others. HDFC bank is no exception and is exploring the new tech in a lot of its applications and functions. With two teams where data science is at the core of their work — marketing analytics and risk analytics — the bank mostly has a centralised data science team. For this week’s feature, we got in touch with Kaushik Ghate, Sr. Vice President and Head, Marketing Analytics at HDFC bank to understand the hiring process of data scientists in its team. 

Skill Sets For Data Scientists At HDFC Bank

Kaushik shared that they look for a whole lot of qualities while recruiting data scientists. Firstly, the incumbent should have a quantitative educational background in fields such as statistics, economics, mathematics, engineering, etc. “Having said that we also have management students, graduates, econometricians in the team. While the educational background is important, it will not restrict a deserving candidate to get a place on the team,” shared Kaushik. 



He further added that there are things that can be taught and things that can’t be taught. The latter ones play an important role in recruitment decisions. 

Secondly, they look for candidates with proficiency in coding in at least one data science language such as R, Python or SAS, among others. “Conceptual grasp of the statistical process is a must-have,” shared Kaushik. Domain knowledge, while desirable, is not a must-have. Apart from that, Kaushik shared that prior experience in advanced analytics and predictive modelling helps, especially for lateral hires. 

Some of the other soft skills required are logical and structured thought process, the ability to conceptualise solutions to problems and good articulation skills. “The capability to understand abstract problems and convert them into data science problems is a big plus,” added Kaushik. 

Interview Process

Explaining the interview process, Kaushik said that the first level of screening is based on the resume, i.e. educational background, skills and kind of work done before. Once the candidate profile is shortlisted, they look for two broad matches — techno-functional match and HR match. For this, during the interview, candidates are evaluated for technical skills while also checking for a match on softer aspects such as aptitude, attitude, enthusiasm, articulation, etc. Profiles that clear this stage are then vetted by HR for fitment.

What are some of the common interview questions asked at HDFC bank? Kaushik is quick to add that they typically start by understanding the career path that the candidate has charted for himself/herself, the kind of work he/she has done and the skills demonstrated. “This by far is the single biggest indicator of a candidate’s clarity of thought (or the lack of it),” shares Kaushik. Technical questions and case studies will follow after that.


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HDFC bank hires a data science candidate in multiple ways. The traditional route is through an HR partner where they do the first level of screening. HDFC bank is also experimenting with hackathons for hiring data scientists. Apart from this, the team also reaches out within its professional network for sourcing talent.

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Data scientists can apply through job openings published by HR or drop their profiles on the website, or reach out through professional platforms like LinkedIn. 

Growth Opportunity For Data Scientists At HDFC Bank

The bank has a very clear motto for all existing and aspiring data scientists — drive customer relevance through data. Therefore, the data scientist is expected to do only one thing — use data to understand what the customer wants. Sometimes this is presented as a simple business problem, but mostly it is a complex problem which needs a simple solution.

Having said that, the bank provides ample opportunities for data scientists to grow. Growth can take various forms such as a bigger role within data science, moving to other business functions, moving into a team leader role, etc. “The growth largely depends on the competencies demonstrated by the candidate and their career aspirations,” said Kaushik. He signed off by wishing the aspiring data scientists all the best and success in their professional endeavours!

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