Openai·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

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.

Questions Asked (1)

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?

A/B Testing & ExperimentationTechnical Trade-offsRoot Cause Analysis

Author's notes

This one tripped me up more than I expected.

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AI HintsAI Generated

Suggested Approach

Start by systematically identifying potential bugs and data quality risks in the script, such as non-reproducible random assignment, lack of idempotency, and missing validation. Then propose a refactored design that ensures deterministic, reproducible group assignment using hashing, proper logging, and safeguards against duplicate offers. Finally, discuss how to validate the new logic and monitor for issues in production.

Pro tip: Emphasize reproducibility and idempotency: use a deterministic hash of user ID and experiment salt, and ensure the assignment and offer logic can be safely retried without side effects. This shows you understand production-grade experimentation systems.

1. Identify bugs and data quality risks

Review the script for issues like non-deterministic random assignment, lack of idempotency, missing validation of user IDs, and potential race conditions. Consider how these could lead to biased experiments or duplicate offers.

2. Propose a reproducible assignment method

Suggest using a deterministic hash function (e.g., MD5, SHA-256) on a combination of user ID and experiment salt to assign groups. This ensures the same user always gets the same group, even across runs or services.

3. Ensure idempotency and safety

Implement checks to avoid triggering the free-trial offer multiple times for the same user, such as storing assignment and offer status in a database with unique constraints. Use transactions or atomic operations to prevent race conditions.

4. Add logging and monitoring

Log all assignment decisions and offer triggers with sufficient detail (user ID, group, timestamp) to enable debugging and auditing. Set up alerts for anomalies like unexpected group distributions or duplicate offers.

5. Validate and test the refactored logic

Write unit tests to verify deterministic assignment, idempotency, and edge cases (e.g., invalid user IDs). Simulate concurrent requests to ensure no duplicate offers. Consider A/A tests to validate the assignment mechanism.

Key Points to Mention

Deterministic hashing for reproducible group assignment (e.g., hash(user_id + salt) % 100)
Idempotency: ensuring the offer is triggered at most once per user, using database constraints or flags
Data quality: validating user IDs, handling missing or malformed data, and logging for auditability
Race conditions: using transactions or atomic operations to prevent duplicate assignments/offers in concurrent environments
Experiment integrity: avoiding bias by ensuring uniform distribution and no interference between groups
Monitoring and alerting: tracking assignment rates, offer redemption, and anomalies to detect issues early

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.