Data science teams are becoming an integral part of most companies, and hiring the right talent while providing them with an environment to grow and explore new technologies has become a central focus for most companies. PayU, a fintech company that provides payment technology to online merchants is no different. It is working towards providing data scientists with the finest exposure that the technology industry has to offer and is focussed on presenting them with exciting opportunities to work with cutting-edge AI-ML technologies.
For our weekly column, we got in touch with Priya Cherian, Chief People Officer at PayU to understand the data science hiring process at the company.
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Data Science Group At PayU
With a centralised team structure from a managerial perspective, Cherian shares that they are decentralised from a functional viewpoint — following a pod structure, i.e. small teams focused on a goal.
“For such teams, we follow a decentralised structure where the data scientist is part of that team, and his/her work is completely dependent on the pod. However, for certain long-term and research projects, we follow a centralised approach where a lot of data scientists and data engineers work on the same project and help resolve problems,” explained Cherian.
Data Science Skill Sets
The company pays a strong focus on a candidate’s grasp on the fundamentals of mathematics and statistics, along with hands-on experience in coding and solving business problems using machine learning techniques. “The candidate should not only have knowledge of the ML algorithms but also should know how and why they work. This allows them to be more flexible when modifying the underlying algorithm,” Cherian pointed out.
On the other hand, when it comes to educational background, Cherian shared that the data scientists in their team come from varied backgrounds, including computer science, mathematics, statistics, and other engineering domains. They look for candidates with a strong background in mathematics and coding first.
What weighs more — skills or education? Cherian is quick to add that skills and understanding of the subject matter are more important than the educational background. “However, for some roles which are more research-oriented, we do look for PhD candidates,” she said.
At the end of the day, they are looking for a smart data scientist, who has the ability to learn new things, has a get-things-done attitude, have skills in math and statistics, knows how to work with numbers and build models, knows databases and programming. “A data scientist should be able to understand how businesses work to find a problem and create a solution for it. They should also have the ability to convince non-data scientists that it’s a good solution to resolve their issue,” shared Cherian on PayU’s definition of an ideal data science candidate.
Data Science Interview Process
Cherian shared that all the data scientists at all the levels go through an initial test. It is then followed by 3 to 4 technical rounds followed by an HR discussion. Once the candidate clears these rounds, he/she is offered a job by PayU.
“Most companies hire software engineers or business analysts and call them data scientists. We avoid those mistakes by understanding the motivations and career aspirations of the candidate. Our interview process is unique in that sense where we focus a lot more on the statistical and mathematical background of the candidate,” shared Cherian.
During the interview sessions at PayU, candidates are usually asked to explain the algorithms in detail and some of the projects that they may have undertaken in previous jobs and deep dive into them.
The company is currently looking to hire for the positions — Data Scientist and one as Data Scientist (Graph) for Mumbai and Bengaluru offices. They further plan to hire at least 5-6 data scientists over 6-9 months.
The majority of our hiring at PayU is done from internal referrals, internal job postings, career pages, and traditional sourcing. A candidate can reach PayU via LinkedIn, career pages, or reaching out to other DS in the company.
Job Role & Growth Opportunities
Cherian shares that a data scientist at PayU is responsible for everything that comes in their domain, including understanding data, Exploratory Data Analysis (EDA), model development, working with stakeholders, model deployment, and monitoring. “Also, we have some research projects, and we align some data scientists to those specific projects as well depending on data scientist’s interest and project requirements,” she said.
Highlighting the growth opportunities for data scientists at PayU, Cherian said that a data scientist depending on his/her interest could grow more in the field of business or research. “We encourage our team to interact very closely with the end-users and build solutions that can be utilised for the benefit of end-users,” she said on a concluding note.