AdaptiLab Blog

Biggest Interview Turn-offs for Machine Learning and Data Science Candidates

Posted by Allen Lu on Sep 10, 2020 5:53:00 PM

We’ve asked several machine learning and data science candidates over the past few months what particular things they dislike about interview processes that they’ve gone through, and here’s what they had to say. We've anonymized their responses for privacy.

Candidate A (Ph.D. Candidate in Machine Learning)

Candidate A is currently in their final year of completing a Ph.D. in Machine Learning and Computer Science at the University of California, Riverside. They previously held machine learning roles at Philips Research North America and Ticketmaster.

“For me the biggest issue with interview processes I’ve gone through is the lack of communication or feedback. I don’t think it’s necessary to give incredibly detailed feedback after interviews, but at the very least I’d like to receive a general sense of how I did on an interview. It also sometimes takes several days or even weeks for companies to get back to me after an interview, which is very annoying especially when I have other interview processes and offer deadlines.”

Candidate B (Computer Vision Research Scientist)

Candidate B is a computer vision and research specialist who’s spent the past 7 years as an engineer and research scientist working on machine learning and computer vision at Luminex Corp.

“I haven’t done as many interviews as probably other candidates who are actively looking, given that I’ve been at the same company for the past 7 or so years. Normally when I do go through interview processes it’s because I found a startup that’s fascinating or because I get contacted by a recruiter. In general, the only things I really dislike about some interview processes is the lack of transparency upfront about the types of projects I’d be working on at the company or a lack of clarity on the size of the team I’d be working on. I usually prefer to know these things near the start of the interview process rather than during an onsite.”

Candidate C (Data Scientist & Quantitative Analyst)

Candidate C is a data scientist and quantitative analyst with experience working on analytics and modeling at Deloitte, as well as data science at Oscar Health.

“I’m not really a fan of interview processes that seem like they have nothing to do with the company or the role. I often do data science interviews but end up being asked basic undergraduate computer science problems involving linked lists or binary trees. It’s fine to test for programming ability if the role requires it, but I’d prefer if the interview tested it in more of a data science context.”

Candidate D (Senior Data Scientist)

Candidate D is a senior data scientist with experience managing teams and doing heavy statistical modeling. They previously held positions at Integral Ad Science and Agentis Energy.

“When it comes to interviewing, I really like interacting with interviewers and getting to know more about the company. Since I mostly interview with startups now, I’m usually able to talk to one of the executives at the company as well as the head decision maker for their data science and machine learning initiatives. The only real dislike I have for a couple of the interview processes that I’ve gone through is when the interviewer doesn’t seem very passionate about the work or convey the company mission well. In that case, I normally just pass on continuing with the interview process.”



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