OpenAI Data Scientist candidates report a recruiter conversation, often a take-home analysis, and a technical or hiring manager discussion centered on SQL, experimentation, and defending their analysis.
$278K
Avg. Base Comp
$800K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describing OpenAI's Data Scientist process most often mention a product analytics take-home followed by a conversation about that work. Two candidates describe analyzing experiment or launch data, such as a ChatGPT Plus trial scenario or a feature launch, and one says AI tools were explicitly allowed. One candidate reports that validating and tailoring the AI-assisted output took longer than expected.
The follow-up discussions vary. One candidate reports a 20-minute walkthrough of the take-home, then SQL questions tied to the experiment and a Java code review where they had to judge whether randomization was set up correctly. Another reports a three-part screen with a presentation, standard analytical SQL (aggregations, window functions, joins, filtering) and a fairly generic debugging exercise. A third describes a 45-minute defense where the interviewer pressed on limitations, outliers and confounding variables.
Other reports are less code-heavy. One candidate had a recruiter call on motivation and background, then a technical round on SQL with CTEs and joins plus A/B testing fundamentals. Another describes a casual hiring manager screen that was a project deep dive covering impact measurement, causal reasoning without an A/B test, and stakeholders.
For preparation, practice explaining the reasoning behind your analysis, not only producing it. Review experiment design and SQL, and practice spotting errors in unfamiliar code. Most reports here end in rejection or a pending outcome, so the sample is small.
Synthesized from 8 candidate reports by our editorial team.
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Real interview reports from people who went through the OpenAI process.
I started with an HR screening call where they asked me to walk through my past research experience and discuss my team preferences. The conversation felt pretty standard — they were trying to get a sense of my background and what I was looking for. After that, I moved into the technical round, which had an interesting twist: they let me pick between three options — a coding problem, an ML coding problem, or an applied statistics question. I chose the applied statistics track, and they presented a scenario involving a distributed computing problem. The idea was to show how statistical knowledge could provide practical insights into solving the issue. It wasn't a straightforward math problem; it was more about thinking through a realistic scenario and explaining how you'd approach it. The whole technical piece felt fair — if you have solid experience with statistics and ML frameworks like PyTorch, you should be able to work through it. The interviewers were professional and the process felt well-structured, though I didn't end up getting an offer. Honestly, the hardest part wasn't the interviews themselves but the waiting afterward — radio silence for days after the onsite made it tough to know where things stood.
Prep tip from this candidate
If you go for the applied statistics track, be ready to discuss how statistical reasoning solves real distributed computing problems—it's not pure theory. Also, familiarize yourself with PyTorch deeply if that's your framework, and don't stress too much about pure coding; the interview flexibility suggests they value domain expertise over algorithm grinding.
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Sourced from candidate reports and verified by our team.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter call covering motivation for OpenAI, reasons for changing roles, background, and an overview of the role, team and process. One candidate also mentions discussion of team structure and location expectations.
Several candidates report a take-home on experiment or launch data, such as a ChatGPT Plus trial or feature launch. Deliverables may include a notebook, doc or slides. One candidate says AI tools were encouraged and that the work took a couple of hours; another spent much of a weekend.
Candidates report presenting the take-home, with questions that may probe limitations, outliers and confounding variables. One reports a 20-minute walkthrough, and another a 45-minute defense. SQL questions tied to the experiment context may follow.
Reported SQL covers CTEs, joins, aggregations and window functions. Two candidates describe code review or debugging, one in Java focused on randomization and missing setup. One needed a hint or two, so spotting issues without prompting may matter.
One candidate reports a casual hiring manager screen built around one impactful project. Follow-ups covered impact measurement, whether an A/B test was run, causal confidence, stakeholders and tradeoffs, with time left for candidate questions.
