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What Is The Hiring Process For Data Scientists At Micron

What Is The Hiring Process For Data Scientists At Micron

Srishti Deoras
W3Schools

With the core data science group based out of Boise, USA, Micron India team began operations from December 2019 to empower the company’s core engineering, manufacturing and business units to make data-driven decisions that are quick, accurate and insightful. With a centralised structure of the data science team, the company believes that an ideal data science candidate should be a digital-native, who is excited about the potential of data to deliver intelligence. 

Data Science Skill Sets At Micron

Koushik Ragavan, Director Data Science at Micron India shares that a strong fundamental grasp of first principles and high levels of competency in applied statistics, computer programming, discrete simulation and database management is a must in the data science candidate. 

“We also look for an ability to communicate complex ideas, navigate Micron’s collaborative and multicultural global ecosystem. Original publications and patents will always get you to the front of the line for an experienced professional, and a great attitude with an ability to upskill quickly are key attributes we look for in a campus hire,” he added.



In terms of educational background, Ragavan said that as they are beginning to build the team, their current focus is on recruiting candidates with substantial experience or ones with postgraduate or doctoral degrees in STEM. Further adding that skills weigh more than education background, Ragavan added that they recently on-boarded 92 candidates from 9 tier-one campuses from the 2019 campus season. They are further looking to offer 240 jobs in the 2020 season.  

“While we have found that the top-tier colleges provide a certain standard of talent, ensuring we get a Day-0 slot in the hiring season makes all the difference,” he said. 

Interview Process At Micron

Ragavan believes that creating data science solutions is their primary responsibility. In order to have the biggest impact in terms of speed, accuracy, reliability, repeatability and scalability of their solution, the practitioners may have to don multiple hats including but not limited to data engineering, data characterisation, software development, development of UI/UX, application support et al. Therefore they follow thorough and extensive screening to hire the right candidate.

“While the weightage may differ from role to role, they are tested primarily for problem-solving, programming, statistics and AI/ML concepts across four to five rounds of interviews with a globally diverse interview panel. For New College Graduates (NCG) we have an online assessment and two to three interviews,” he said. 

They ask questions related to the candidate’s current areas of interest/experience, basic and advanced ML techniques, STAR (Situation, Task, Action Results) from their earlier experiences and more. 

Ragavan shares that while top-tier engineering colleges provide a large part of the sourcing pool, for experienced candidates they rely on Micron’s career portal, external job boards like LinkedIn and internal referrals from the primary pipelines. The best way to look for data science opportunities at Micron is to visit their career portal

Micron believes in recruiting for diversity. “Not recruiting for diversity would be short-sighted. People from different backgrounds ensure you have a wide range of ideas and viewpoints to choose from,” said Ragavan. 

Wrapping Up

Having said that, recruiting data scientists has its own set of challenges. Ragavan shares that most candidates, both at the entry-level and experienced, tend to take the easy way out by becoming black box users of the methods and practices without understanding the methods and practices themselves. 

While they may be able to execute transactional solutions, they will not be able to develop strategic applications for high-value problems. It is extremely critical for them to go beyond desired outputs, to understand the “why, when, what, where, how” of a problem statement before they zero-in on the chosen approach,” he said. 

For anyone looking to venture into this field Ragavan advises to avoid being black box users. “Roll up your sleeves and get down into the trenches,” he said on a concluding note. 

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