OpenAI Data Scientist Interview Experience — A LinkedIn Cold Ask, a Take-Home, and a Python Debug Stumble

I didn't expect to actually get an interview invite from OpenAI — I'd only asked a recruiter about it on LinkedIn. My own level isn't really there, so I went into it purely as an experience, but the experience was great, worth writing up a post-interview reflection.

Some background: I do data work at a big company, and my job search has mainly focused on experimentation and causal inference. After talking with HR, they put me on the DS / ML analytics track, which I felt was a pretty good fit. Round 0 was a take-home — a really good experiment design question about the impact of a free-month promotion. Personally I felt a simple, rough analysis of retention and signup rate was nowhere near enough — there were countless pitfalls dug into it, and if you know where the pitfalls are, you're probably fine. There's no single "correct" algorithm for the ROI calculation — anything with sound reasoning works.

On the bar for passing: after I submitted, I found spots where my own analysis wasn't quite right, more than one, so you don't have to get everything 100% correct — no need to worry about that. But missing some fundamental flaws might cost you points, while a creative analytical approach earns you a lot of credit. The question definitely doesn't hand you all the data you'd want; being able to state your own assumptions and solve the problem within the given limits earns a lot of credit too.

In the first-round discussion, we barely talked about the take-home itself. The interviewer picked two of the questions from it and had me walk through my logic and how I'd present it to a stakeholder, and that was it — no further questions. I expected to get asked about the spots where I'd miscalculated, but that never came up; might just depend on the interviewer. My takeaway is that as long as you can explain your thinking clearly with solid reasoning, that's enough. The SQL part wasn't hard — I overcomplicated it at first, writing out logic for qualifying users into the experiment, but the interviewer said to just assume the table already covers that logic, and then it turned out to be very simple. They asked about an edge case around repeat churn — handle it in SQL and you're fine, not hard. Lesson for next time: confirm assumptions with the interviewer up front — I took things for granted this round and didn't ask beforehand, which cost me some time. Where I mainly flunked was the Python debugging. I'd never paid attention to the treatment-assignment process before — I'd always just analyzed the results after the fact — so looking at that code left me a bit lost, and I only found the result with hints. The bug was fairly obvious, so needing hints for it really shouldn't have happened.

To sum up: if you get the chance, everyone should give it a shot. Top companies are obviously looking for the best of the best, but that doesn't stop you from stretching your own thinking — being handed data and a question to work through, and reviewing AB testing again in the process, I feel like I covered basically every pitfall I might run into. Pretty worth it, I think.