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What Is The Hiring Process Of Data Scientists At

What Is The Hiring Process Of Data Scientists At

As data science aspirants scramble to upskill online to make productive use of the lockdown period, a well-known recruitment portal in India is looking elsewhere. For Chief Analytics Officer at, Madhukar Kumar, a good attitude precedes everything else. 

“Skill sets can be acquired, but attitude cannot. A person with a positive – and learning – attitude can accomplish anything,” he believes.

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In fact, Kumar conceded that he does not lay a lot of emphasis on a candidate’s educational background. Instead, relevant certifications seem to work for the company. “Earlier, I used to give very high weightage to their educational background, but I have burnt my fingers and now it is no longer my primary criterion” says Kumar. 

So what does an ideal candidate for Kumar look like?

Ideal Data Science Candidate At

In addition to a great attitude and a positive mindset, Kumar has identified four key areas which help him shortlist candidates for data science positions at These are:

  1. Problem-solving skills: Data science is not just about algorithms and advanced technical skills – being good at solving real-world problems using data is just as critical. According to Kumar, it is a quality data scientists generally lack because they are “too focused on applying machine learning and deep learning algorithms, and often lose sight of the bigger picture”.
  2. Application of algorithms: Closely following the ability to solve problems is hands-on experience of applying machine learning algorithms. Explains Kumar, “I ask simple and very basic questions to evaluate a candidate’s understanding on fundamentals. If someone is talking about deep learning algorithms, but cannot write a simple logistic equation, that is a red alert for me”.
  3. Communication & business acumen: In addition to the above two, candidates’ ability to effectively explain their projects where they have applied ML/DL to solve any business problem is also important.
  4. Python coding language: Although not mandatory, as per Kumar, it is a good skill to have. This is because it provides a wide gamut of applications in data science. 

“To sum it, my ideal candidate would be someone with a positive attitude, a problem-solver, someone with excellent hands-on experience in ML/DL, and good communication and python coding skills.”

ALSO READ: What is the hiring process for data scientists at LinkedIn?

Typical Hiring Process At

The company generally follows a simple two-step process for its technical hiring needs. This includes a round with a Senior Data Scientist, followed by a technical and fitment round with the Chief Data Scientist. Both will roughly be for about an hour each.

It hires throughout the year, and onboards candidates as and when the right talent arises. “We employ several non-traditional ways to recruit, but the key method is to homegrown the right talent within the platform and move them to the analytics team,” adds Kumar.

In its experience thus far, has the company faced any challenges when it comes to hiring for its data science positions?

“Yes, hiring good data scientists and finding the right candidate to fulfill all requirements is becoming challenging by the day,” says Kumar. “These days, people are more fascinated with using and applying deep learning algorithms without having an understanding of basic ML algorithms,” he adds.

For the company, lack of fundamentals has been one of the main challenges. “Candidates come bragging about their deep learning projects and experience without having knowledge of basic fundamentals,” he adds.

See Also needs data support to fix search and matching problems. The platform reportedly has 0.3 million jobs and 8 million active candidates. Appointed data scientists need to explore this data on a daily basis. “Most candidates do not have such experience and that is another big challenge,” says Kumar.

ALSO READ: What is the hiring process for data scientists at IBM?

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The company admits that it prefers face-to-face interviews over those conducted telephonically. According to them, that is important to gauge a candidate’s overall attitude and approach towards problem-solving.

“As mentioned earlier, it is also important to check for basic fundamentals,” says Kumar. He explains, “Most people today are using transfer learning in most scenarios wherever they are mentioning deep learning. So, the knowledge is mostly limited to how they have run the code and got the result. Real data scientists will always try to find the right answers and not only limit themselves to running codes and getting the output.”

This level of screening may be required for the breadth of responsibilities a data scientist at may be expected to take on. It includes, but is not limited to improving search (both recruiter and candidate), building inhouse parsers, improving algorithms for Ownbase mailers, and building new products with heavy usage of AI.

“A successful candidate should expect heavy hands-on work in these challenging areas,” says Kumar. “The best part of working with Shine data science team is that there is no dearth of opportunity – they will be involved in each and every process within the organisation that needs improvement,” he adds.

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