OpenAI Data Scientist interview questions

14 Data Scientists have shared notes from their OpenAI interviews, with the questions they were asked in each round. That is 2% of the 667 OpenAI interview notes on Screna AI.

14 Data Scientist interview notes

OpenAI·Data Scientist·Technical Phone Screen

Jul 2026
SQL-heavy technical screen for a DS role at OpenAI. The whole thing was basically one long experiment analysis problem with a bunch of moving parts: ITT analysis, triggered analysis, and a data quality audit all in one sitting. Felt like a take-home crammed into a live session.
  • Write a SQL query to compute the intent-to-treat paid conversion rate within 60 days of first exposure, broken down by variant (control vs. trial), for users first exposed between June 1 and June 30, 2025. Each user should be counted once using only their first exposure, and users who had any payment before their first exposure must be excluded. Return variant, exposed user count, converters within 60 days, conversion rate, and 95% Wald confidence intervals.
  • Write a second SQL query for a triggered analysis, restricting to users in the trial variant who actually started a trial, and compute their paid conversion rate within 60 days of first exposure.
  • Write a data quality SQL query that flags the following anomalies: users who have a trial record but no prior exposure, users with multiple exposures on the same calendar day, exposure timestamps that come after trial_started_at, and duplicate user_ids in the users table.

“The leakage exclusion is where I got tripped up first.”

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OpenAI·Data Scientist·Technical Phone Screen

Jun 2026
Brutal take-home style question for a Data Scientist role at OpenAI, basically a full debugging and ML systems design exercise wrapped into one prompt. The kind of thing where you either know your PyTorch internals cold or you're guessing. No fluff, no behavioral warmup.
  • Given a broken minimal causal language model in PyTorch where training loss never improves and sometimes goes NaN, identify every bug across the architecture, masking, numerics, and training loop, explain what each bug causes and why, and provide a corrected implementation.
  • Write three PyTest-style unit tests that would catch the bugs in the faulty model before training starts, covering things like tensor shapes, mask correctness, and gradient flow.
  • Derive the time and memory complexity of scaled dot-product attention in terms of batch size B, number of heads H, sequence length S, and head dimension d_k.
  • Propose two changes that reduce memory usage during attention without significantly degrading perplexity, and discuss the trade-offs of each.

“This was the meat of the whole thing.”

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OpenAI·Data Scientist·Technical Phone Screen

Jun 2026
A/B testing case study for a Data Scientist role at OpenAI. The whole thing was structured around a free trial experiment for paid conversions, and they wanted you to walk through the full analysis from metric definition to a go/no-go call. Pretty intense for what I expected to be a more conversational round.
  • Calculate the paid conversion rate for both the treatment and control groups, then compute the percentage lift between them.
  • Run a significance test on the observed lift. State which test you're using, define your null and alternative hypotheses, and provide either a p-value or confidence interval.
  • Compare cancellation rates during the trial period versus after the first paid billing cycle for each group.
  • Estimate the net change in paid subscribers at 30 days and 60 days, accounting for both new signups and cancellations.
  • What additional metrics or user segments would you look at before recommending a full rollout of this feature?
  • Summarize the experiment results and give a clear go or no-go recommendation backed by the numbers you calculated.

“Pretty mechanical but I fumbled the lift formula for a second.”

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OpenAI·Data Scientist·Technical Phone Screen

Jun 2026
SQL-heavy technical screen for a DS role at OpenAI. One question, but it was dense enough to chew on for a while. The kind of problem where you think you're done and then realize you forgot to handle the multi-trial-start edge case.
  • You're analyzing an A/B test for a free trial campaign. Given tables for experiment assignments, trial starts, and user sessions, write a single SQL query that returns per-variant counts of assigned users, converted users, signup rate, retained users, and retention rate among converters. Conversion is defined as starting a trial within 7 days of assignment, and D30 retention means having at least one session in the window 30 to 37 days after the trial started. Use the earliest qualifying trial start if a user has multiple.

“The multi-trial-start rule is where I got tripped up.”

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OpenAI·Data Scientist·Technical Phone Screen

Jun 2026
Got a code review style question for a Data Scientist role at OpenAI where they handed me a Python script and asked me to tear it apart. Pretty technical for a DS interview but I guess that's OpenAI for you.
  • You're given a Python script that assigns users to experiment groups and triggers a free-trial offer. What bugs or data quality risks do you see, and how would you refactor it to make the assignment logic safer and more reproducible?

“This one tripped me up more than I expected.”

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OpenAI·Data Scientist·Technical Phone Screen

May 2026
Got a case-style question at what seemed like a technical screen for a DS role at OpenAI. The whole thing was one big experiment design problem about a subscription promotion, and it went pretty deep into causal inference, ROI estimation, and stakeholder communication. Felt like a take-home dressed up as a live interview.
  • An online subscription product wants to test giving eligible new users their first month free instead of charging upfront. Design a full experiment and analysis plan to estimate the causal impact of this promotion.
  • What should the primary decision metric be for this experiment, and why are simpler metrics like signup rate or retention among signups not enough on their own?
  • How would you measure both short-term and long-term impact when the free month shifts the timing of revenue and changes who signs up?
  • How would you estimate ROI when the first month is free and some downstream revenue data is missing or only partially observed?
  • What are the key pitfalls in this kind of experiment, covering things like selection bias, delayed conversion, seasonality, cannibalization, and heterogeneous treatment effects?
  • If your dataset is limited or imperfect, what assumptions or sensitivity analyses would you run?
  • How would you present your recommendation from this experiment to a non-technical stakeholder?

“This is a monster of a question.”

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OpenAI·Data Scientist·Take-home Assignment

May 2026
SQL take-home for a Data Scientist role at OpenAI. One question, pretty involved, centered on building an experiment metrics report from scratch. The problem was well-scoped but had enough edge cases to trip you up if you rushed.
  • Write a SQL query to measure the performance of a free-month promotion experiment. Given an experiment_users table and a subscription_events table, return one row per variant with counts for eligible users, signups within 30 days, paid conversions within 60 days, churn within 90 days of first paid start, and a churn rate column.

“The churn definition is what got me.”

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OpenAI·Data Scientist·Take-home Assignment

May 2026
Got a take-home style SQL problem for a Data Scientist role at OpenAI, centered on analyzing a free trial A/B test from raw event logs. The schema was realistic and messy in the way production tables actually are, which I appreciated even if it made the metric definitions trickier to implement cleanly.
  • Given raw event log tables for experiment assignments, offer exposures, subscription lifecycle events, and app sessions, write SQL that outputs one row per variant showing assigned users, 7-day trial signups, signup rate, D30 retained users, and retention rate.
  • What does an upstream data pipeline need to contain to make these A/B test metrics reliably computable?

“The metric definitions looked clean on paper but the SQL got complicated fast.”

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OpenAI·Data Scientist·Onsite - System Design / Architecture

May 2026
Onsite system design round at OpenAI for a data scientist role. The main question was a deep code review slash experiment reliability problem that required knowing a lot more than just stats. Pretty grueling if you haven't thought carefully about experiment infrastructure before.
  • You're given a Python snippet that randomly assigns users to a trial or control group and triggers a billing API call. Find at least 8 correctness or reliability issues that could corrupt the experiment or its data, and propose concrete fixes for each.

“This wrecked me a little at first because I came in expecting a stats question and got handed code to audit.”

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OpenAI·Data Scientist·Technical Phone Screen

Apr 2026
Interviewed for a Data Scientist role at OpenAI and the technical portion was heavily focused on subscription event pipelines, schema design, and some code review work. Felt like a solid systems-thinking test more than a pure SQL grind.
  • Design a raw event table schema that can support deriving a user's current subscription status as well as their full historical state over time.
  • Write SQL or Python logic that produces one row per user showing their latest subscription status, correctly handling the case where a user has signed up, cancelled, and signed up again.
  • Review a Python snippet that assigns experiment variants and delivers a free-trial offer. Identify at least three improvements or safeguards you would add.

“This felt like a warmup but it actually took me a second to think through.”

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