Reported OpenAI Data Scientist interviews include an initial conversation, a product-analytics take-home, and technical follow-up on the work, SQL, experimentation, and debugging.
$278K
Avg. Base Comp
$790K
Avg. Total Comp
3 rounds
Typical Rounds
2-4 weeks
Process Length
OpenAI Data Scientist reports describe an analytics-focused process for product and experimentation work. Initial conversations may be with a recruiter or hiring manager. Reported recruiter discussions cover background, motivation, role context, and logistics. One hiring-manager screen focused on an impactful past project, with follow-up questions about the business problem, measurement, stakeholders, tradeoffs, and results. Prepare a clear project narrative that can support detailed questioning.
Several candidates report an independent take-home involving a large product dataset, experiment, feature launch, or user behavior. Deliverables have included a notebook, document, or slide deck. Reported tasks include exploratory analysis, segmentation, experiment insights, and business recommendations. AI tools were permitted in some accounts. Be ready to validate and explain your own analysis, including its assumptions and limitations, rather than relying on generated output.
Technical follow-ups have included presenting the take-home, analytical SQL, experimentation, and debugging or code review. Candidates report joins, CTEs, aggregations, filtering, and window functions, along with A/B-test design, metrics, hypothesis testing, and interpretation. A reported defense probed outliers, confounding variables, and a changed data constraint. One code-review exercise asked the candidate to assess experiment randomization and missing setup details.
Synthesized from 7 candidate reports by our editorial team.
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Real interview reports from people who went through the OpenAI process.
I was interviewing for senior data scientist roles with a product analytics and experimentation focus. OpenAI was one of the more interesting processes I went through, mostly because of how different the take-home was from anything I'd seen before.
After the initial recruiter call, they sent a take-home assessment directly. No separate technical screen first. The assessment was pretty detailed: they gave a scenario around launching a new trial for ChatGPT Plus, provided a big dataset, and asked me to generate insights from it. You could use any tools you wanted, including AI, and they wanted either a working notebook, a doc, or a slide deck with the insights.
The AI-assisted format was interesting because it shifts what they're actually evaluating. The first half came together faster with AI help, but tailoring it, making it concise, validating everything, and making sure I was asking the right questions took longer than expected. I spent a good chunk of a weekend on it.
After submitting, they set up a technical screen with one of their data scientists. The first 20 minutes was a walkthrough of the take-home presentation, answering questions on it. Then the rest of the interview covered SQL questions (related to the experiment context from the assessment) and a code review exercise. The code review was in Java, which I'm not super familiar with. They gave me the code and asked me to find errors, evaluate whether the randomization was set up correctly, and flag anything missing. The framing was: you're working with an engineer who set up this experiment, here's the code, what do you find?
I finished the code review but needed one or two hints along the way. My read is that they were probably expecting me to get there faster and without any prompting. The assessment part seemed to go okay based on the interviewer's reaction, but the code review is where I think I fell short.
With OpenAI there's a lot of competition, so even doing okay isn't enough. You really need to exceed expectations. For the code review specifically, being able to quickly spot issues in unfamiliar languages without needing hints seems to be the bar. The take-home is also genuinely time-intensive, so plan for a full weekend if you want to do it well.
Prep tip from this candidate
The code review round uses unfamiliar languages like Java and tests whether you can independently spot randomization errors and missing experiment setup details without hints. Practice reviewing experiment code for statistical validity (randomization, assignment logic, edge cases) in languages outside your comfort zone, and aim to identify issues quickly without prompting.
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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 either a recruiter conversation or a hiring-manager screen. Recruiter calls covered background, motivation, role context, and logistics. One hiring-manager interview centered on an impactful project and used follow-up questions to explore business context, measurement, stakeholder management, tradeoffs, and results.
Several reports describe a take-home assignment analyzing a large dataset related to a product experiment, trial, feature launch, or user behavior. Candidates submitted a notebook, document, or slides. Reported work included exploratory analysis, segmentation, experiment insights, and actionable recommendations; AI tools were permitted in some accounts.
Reported follow-ups include a take-home presentation or defense, SQL questions, and debugging or code review. SQL topics included joins, CTEs, aggregations, filtering, and window functions. Experiment discussions covered design, metrics, hypothesis testing, interpretation, outliers, confounding variables, and limitations. Some candidates also reviewed code for errors or experiment setup issues.
