The biggest mistake companies make is interviewing data scientists like software engineers

“Great question. And so, again, this is biased predominantly towards sort of tech companies, but I think the biggest one is actually interviewing them as if they were software engineers. And so, in particular, the fellows, when they graduate, the program go into interviews, and a big portion, sometimes even the large majority of those interviews at most companies, for data scientist roles, are nevertheless sort of whiteboard coding interviews, That are essentially testing, like, computer science one-on-one skills, like algorithms and data structures. And, you know, I'm not saying those aren't important. They are, and they should be picked up, but they're almost used by companies as, like, intelligence tests. Are you smart? Where smart means, do you know computer science fundamentals? And the answer is, yes, they are smart, but no, they didn't study computer science, and therefore this is very, ah foreign territory. And so, you know, as we go, the program is focused on just doing great data science, but as we go into those last, sort of, those interviews, people start Kind of prepping for those interviews. But, you know, really, sometimes it almost feels like it's kind of like the SAT. Like, you're just trying to study to pass that test, because a lot of the work afterwards as a data scientist is not really about coding up, you know, heaps or on the whiteboard. And so that, that's one thing I think that, you know, if I had advice for hiring managers, would it, like, slant the interview process to be more reflective of what a data scientist actually does, as opposed to just kind of a, Use the same process you would for software engineers.”

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