Frontier AI / foundation models Data Scientist Interview Loop
OpenAI is the frontier lab with enough candidate reports to describe its data scientist interview. Anthropic's isn't publicly reported. The pattern that shows up is SQL, a product and experiments case, and a take-home with a review. There's also an AI-specific twist. You critique AI-written code, and you judge how a model behaves, not only how users behave. OpenAI's version is a recruiter and hiring-manager screen, then a 48-hour take-home where AI tools are encouraged. A review follows that pairs your presentation with debugging AI-written SQL or Python. The final loop is product and metrics cases.
Practice by round
SQL basics on AI product data, like token usage, cost by model tier and repeat use within a session. Then quick statistics coding.
Medium and hard SQL, like retention, sessions from timestamps and conversion windows, plus statistics coding for experiments. Practice the SQL in debug mode. It's the closest practice to reviewing AI-written code.
Product cases for AI features, and mini-analyses where you define the metric yourself: Model Version Quality, Model Preference Win Rates, RAG Answer Support and ChatGPT Stickiness Test.
| Case study | Difficulty |
|---|---|
| AI Assistant Success Metrics | Medium |
| Measuring AI Summary Usefulness | Medium |
| Moderation Threshold & Error Costs | Medium |
| Adoption vs. Content Quality | Medium |
| AI Feature Engagement Drop | Medium |
| AI-Assisted vs. Classic Search | Medium |
| Liked Feature, Flat Business Metric | Medium |
| Shipping a Regressive-Segment Model | Hard |
| Data project | Difficulty |
|---|---|
| ChatGPT Stickiness Test | Easy |
| Model Version Quality | Medium |
The statistics of judging models and features: win rates, slices, randomization units, and sample size when feedback is sparse.
| Question | Difficulty |
|---|---|
| Drift-Triggered Retraining | Medium |
| Monitoring Without Fast Labels | Medium |
| Sample Size for a Thumbs-Up Change | Medium |
| Estimating pass@k From Samples | Hard |
| User Randomization, Conversation Metric | Hard |