What Is The Hiring Process Of Data Scientists At Mindtree?


Layoffs, furloughs and pay-cuts might be the order of the day at some companies, but there has never been a better time to see data science hiring through a different lens. Not only is it prudent to dive deeper into business processes to pare costs and plan ahead, paucity of adequate skills indicates that these positions are frequently open in top companies till they find the right talent.

“People with the right understanding and skill-sets of data analysis are in high demand,” says Manoj Karanth, VP-Digital at Mindtree. “That is why hiring is usually done throughout the year for experienced folks, though the number of positions vary by business demand,” he adds.

While it is not new to employ non-traditional means to hire data scientists, or even continually engage in good talent whenever they emerge, the data science hiring process at this company may stand testament to the fact that skills are paramount for these positions.


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Ideal Candidate For Mindtree

According to the company, the key challenge for a data scientist is to yoke insights with business outcomes. For Karanth, an ideal candidate is someone who understands the “rhythm of data”. He explains:

“In this job role, nearly 80% of the initial time goes in preparing data, analysing the different slices of data, enquiring on them, understanding the rhythm of the underlying data, before you really apply algorithms and techniques.”

At mindtree, a data science candidate should possess the following skill-sets:

  • Ability to understand the core business problem and frame data-driven outcomes to address that problem
  • Ability to enquire and identify the patterns by slicing the data
  • Knowledge of different algorithms, techniques, and interpretation of the model results
  • Programming knowledge of R/Python
  • Grasp on the problem domains in which the data scientist has previously worked 

Adds Karanth, “While hiring a data science engineer, in particular, we check a candidate’s knowledge of Python programming with the help of platforms like Hacker Rank. For a machine learning engineer or analysis role, we look at skills in data manipulation and programming knowledge of Python.”

ALSO READ: An Analytics Head Guide To Navigating The Downturn

Hiring Process For A Data Scientist

Although the interview style and questions may vary from candidate to candidate, the process of hiring largely remains the same.

“We ideally look for candidates having masters in Math/Statistics/Econometrics,” says Karanth. “Generally, certification of a good academic background in Statistics is required. Candidates having good domain knowledge or good data analysis experience holding a certification might have a higher chance of getting hired,” he adds.

Looking to apply for a data science position at Mindtree? If so, be prepared for the hiring process there:

  • Step 1: Telephonic conversation to assess basic skills
  • Step 2: Case study driven In-depth technical discussion
  • Step 3: Discussion on domain knowledge and overall capabilities
  • Step 4: Final round of discussion with the HR

As per Karanth, a candidate’s profile is assessed during the initial discussion, including their educational background, skill-sets and basic understanding of data by asking contextual questions. “However, in the subsequent rounds, we take a case-study route to understand the applicability better,” he adds.

Generally, the entry-level hiring is done by engaging with institutions having a good instruction curriculum on data sciences. On the other hand, senior-level hiring is usually based on external applicants and references from existing employees or acquaintances from the industry.

ALSO READ: Mindtree Partners With IISc Bangalore To Advance Research in AI

Data Science Opportunities At Mindtree

As soon as a candidate is hired, the company takes them through a series of continuous learning programs to amplify their current skills and polish them further. These programs include various sessions, like learning programming with Python & spark, working with large data sets, clouds, production deployments, Model maintenance, DevOps CI/CD, NLP, deep Learning, and other emerging areas of AI.

“We believe that continuous learning is the only way to remain relevant and hence, ensure that our team members engage with the customers, understand their problems and then look forward to finding solutions to their problems,” says Karanth.

But despite the demand, finding the right talent who has adequate knowledge and experience has been challenging.

“There are many candidates having a number of certifications, but with no real project experience or applicability,” says Karanth. “For data scientists specifically, we also consider a background in statistics, coupled with a good problem-solving approach,” he adds.

Building on the last point, he says that skills notwithstanding, it is just as important to check the willingness of the candidate to get their hands dirty on slicing and enquiring on the data. “If that willingness is not there, then their ability to find new insights becomes limited,” he adds.

According to him, one of the most common mistakes across the industry when hiring for data science positions is to only consider skills in algorithms and ignore the overall package.

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Anu Thomas
Anu is a writer who stews in existential angst and actively seeks what’s broken. Lover of avant-garde films and BoJack Horseman fan theories, she has previously worked for Economic Times. Contact: anu.thomas@analyticsindiamag.com

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