The story of a dream data science team, CIO News, ET CIO

We often ask that when Indians can run companies like Google, Microsoft, Twitter and Adobe, why can’t India build another Google and Microsoft. And the question is fair, given that all of these leaders were born in India and graduated here before succeeding in the United States. Another question many Indian companies are grappling with is how to attract top tech talent heading overseas. When it’s impossible to match your competitors’ budgets, can you still build great teams?

The question becomes even more relevant for the small community of data scientists. While the term data scientist is often used loosely to describe anyone who can understand and create advanced databases, true data scientists skilled in statistics, programming, and domain knowledge remain hard to find.

Still, some companies tend to build great data science teams, while others can’t, despite having much higher budgets. What is the secret sauce? A sense of purpose, many would tell you. But it is easier said than done. How do you actually instill that sense of purpose in a team? How do you make them work closely together and perform well?

To answer some of these questions, we spoke to some of India’s top companies with excellent data science teams. And today’s main story highlights their internal journeys and their “art” of building great moments.

How do you build a great team? Share with us.

Greetings
Varun Aggarwal
Editor, ETCIO
[email protected]

How do you build your dream data science team?

Today, data science has become a new collar job and these new collar professionals are not readily available. Most new emerging professionals have very good computer programming and math/statistics skills, but they lack domain expertise. Those who have spent time in the industry have knowledge in the area but do not necessarily have the other two.
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What it took for Bigbasket to build a great data science team

For Subramanian MS, head of category marketing and analytics at Bigbasket, a good data science team needs to bring together a set of complementary skills to deliver data science work products.

“Developing and delivering a data science work product such as a smart shopping cart or recommender system requires a combination of skills – domain knowledge, technical knowledge, engineering knowledge, and analytical skills. Therefore, a good data science team must bring together skills spanning multiple roles. Based on the data science problem being solved, smaller teams should be created with relevant skills from the larger team,” he said.
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Generalist or specialist: who should you choose for your data science team?

A function like risk analysis will always have specialists, but marketing analysis will most likely have generalists. However, the number depends on the scale of the business problem we are trying to solve.

For a bank operating in 120 countries, 5 centralized teams spread across the globe, with massive data science needs for countries, business issues, geographies, etc. and at any time would need generalists. In this case, the whole work of generalists can be divided into several categories of specialists because the scale is enormous.
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Vedanta Blueprint for Upgrading Analysis Team Skills

While it’s hard to work and build a model, building large-scale predictive models for a multi-billion company is an uphill battle. However, Vedanta seems to have it all figured out. The company started working on its data in 2016, built the data lake in 2019, is working with a few predictive models and is now embarking on another adventure of building large-scale predictive models for the entire business group. companies.
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