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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Topics based on recent interview experiences.
| Question | |
|---|---|
| Instagram TV Success | |
| Group Success | |
| Google Maps Improvement | |
| Resumable Fact Table Load | |
| Hurdles In Data Projects | |
| Transformer Encoder Layer | |
| Causal Inference Without A/B | |
| Skewed Pricing | |
| Messenger Service Design | |
| Data Pipelines and Aggregation | |
| Unlimited Plan Abuse | |
| Scalable Data Pipelines | |
| Facebook Story Success | |
| Spanish Scrabble | |
| Weighted Average With Missing Dates | |
| LRU Cache 1 | |
| Trial Test Analysis | |
| Statistically Significant Test | |
| Programming Risk Combat | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Employee Salaries | |
| Merge Sorted Lists | |
| Top Three Salaries | |
| First to Six | |
| Closest SAT Scores | |
| Monthly Customer Report | |
| 500 Cards | |
| Experiment Validity |
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.
