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Data Science Hiring Process At Bigbasket

Data Science Hiring Process At Bigbasket

  • Data scientists at Bigbasket have to juggle multiple responsibilities since they are part of a small team supporting a growing and dynamic startup business.

The analytics and data science at Bigbasket is centrally located. Set up in 2013, the 10-member team works with various partners to focus on key business deliverables: analytics work products (reports, dashboards, deep-dives, etc. used by business teams directly) and data science work products (ML models, and OR models that power product features that benefit customers). The team also manages the end-to-end analytics ecosystem, including analytics infrastructure, analytics data pipelines, and deployment of analytical solutions and data science models. 

To understand more about the data science team and the hiring process, we got in touch with Subramanian MS, head of category marketing and analytics at Bigbasket. 

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Required Skills

Subramanian said deep expertise and problem-solving skills are a must in an analytics professional. The expertise includes a solid understanding of algorithms, operation research and machine learning skills. “Some of the overarching traits we look for in all recruits include attention to detail, ownership and proactiveness etc. We believe these are key traits to succeed in the fast-paced, startup environment of Bigbasket,” he added. 

In terms of educational background, Bigbasket looks for engineering graduates with or without experience. As Subramanian shares, many of their team members have pursued and completed advanced machine learning and artificial intelligence programs before joining Bigbasket or, in some cases, while at Bigbasket.

Subramanian said some of the vital skills are problem-solving, analytical thinking, communication skills, attention to detail, ownership and proactiveness. Educational background is further used as a filter to shortlist candidates.

Interview Process 

Subramanian detailed the interview process for candidates, both experienced and fresher. 

The interview process for experienced candidates:

  • SQL test since it is a critical skill used every day by analytics and data science professionals
  • Setting up interviews with other team members to understand the cultural and experiential fit

The interview process for fresh hires:

  • An assessment focused on understanding a candidate’s analytical, reasoning and verbal skills 
  • Setting up interviews to help the candidate understand more about the opportunity and the interviewers to assess if the candidate will be a good fit

The traditional methods Bigbasket relies on recruiting are campus hiring, sourcing candidates through hiring partners and referral from Bigbasket employees. The non-traditional methods include a partnership with entities that offer online programs in analytics and data science, and social media, including LinkedIn.

Candidates interested in a career with the analytics and data science team at Bigbasket can connect with the company directly during campus hiring. They can also connect with individual team members on LinkedIn.

Interview Questions

For fresh hires at Bigbasket, the questions are focused on:

  • Helping them understand the organisation, the team and the opportunity
  • Discussions on analytical problems with a focus on understanding the process they adopt to solve problems rather than solutions to the problems themselves
  • Deep dive into projects – academic or internship to understand their critical thinking skills

For lateral hires, the questions are focused on:

  • Helping them understand their role and the opportunities provided by Bigbasket to develop end-to-end knowledge of an analytics ecosystem
  • Deep dive into prior work done by the individuals with the intent to understand the application of critical thinking and analytical approach to problem-solving

Data Scientist At Bigbasket

Data scientists at Bigbasket have to juggle multiple responsibilities since they are part of a small team supporting a growing and dynamic startup business. Some of the areas the team work on are: 

  • Solving business problems
  • Building analytical work products
  • Deploying production-ready data science models while managing the analytics infrastructure and data engineering pipelines 

Subramanian said senior team members also become an integral part of crucial growth and strategic initiatives running in the company. The initiatives are usually cross-functional efforts that allow team members to work with our founders and senior leaders. This will enable the team members to understand and absorb the leaders’ strategic thinking that power the organisation’s growth.

The company provides ample growth opportunities and allocates independent responsibilities. For instance, shared Subramanian, one of their senior team members, and her team own all analytics and data science deliverables for bb Daily, their micro delivery business. Another senior team member and her team own all analytics and data science deliverables for bb Instant, the smart vending machine business. “These team members began to shoulder these responsibilities within 3-4 years of joining us from campus,” he added.

Hiring Challenges

“A key challenge faced by us is finding candidates who are passionate about using data to deliver impact to the business while showing ownership, proactiveness and attention to detail,” said Subramanian. 

In some cases, there are expectations mismatches between the candidates and the organisation. “We constantly learn from our experiences to fine-tune our search for suitable candidates while also ensuring that we provide more than enough information and opportunities to potential candidates to learn about us and assess if we will be a great fit for them,” he added. 

Pro Tips

For candidates interested in joining the analytics profession, Subramanian suggests focusing on learning the fundamentals of analytics such as algorithms, models and SQL while developing other critical skills such as communication, presentation and problem-solving. It is also essential to understand that a typical work portfolio of analytics professionals includes a mix of data engineering, business analysis and data science. “Those who can succeed in juggling the multiple facets of an analytics work portfolio are most likely to thrive in a career in the analytics industry,” he said. 

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