Metrics question
What north-star and guardrail metrics would you use to judge whether Statsig is becoming the trusted default for how OpenAI teams ship? Include adoption, reliability, and decision-quality measures, and explain how you would review them in a regular operating cadence.
- OpenAI
- Metrics
- Hard
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What this question tests
Whether you can define a metrics framework for an internal platform team, separating adoption vanity metrics from evidence that Statsig is actually the trusted default tool.
How to approach it
- Define trusted default concretely: most experiments at OpenAI run through Statsig by choice, not mandate, and teams do not fall back to ad hoc analysis.
- Pick a north star like percentage of shipped features that went through a Statsig-run experiment or flag, since it captures adoption and habitual use together.
- Add guardrails: experiment setup time, rollback time after a bad flag, and data pipeline correctness incidents, since reliability failures destroy trust fast.
- Add decision-quality measures: percentage of experiments with a pre-registered success metric, and rate of ship decisions later reversed due to a missed guardrail.
- Review weekly in an operating cadence with platform, data science, and infra leads, escalating any guardrail breach immediately rather than waiting for the review.
What a strong answer includes
- Separates adoption (usage volume) from trust (do teams choose it unprompted, do they trust the readout without re-checking manually).
- Includes a reliability guardrail, since one bad exposure-logging incident can undo months of adoption.
- Ties decision quality to pre-registration, a concrete and checkable signal instead of a soft claim about better decisions.
Common mistakes
- Only tracks usage counts, which rewards mandated adoption rather than trust.
- Skips a reliability or guardrail metric entirely.
Likely follow-up questions
- What would you do if usage is high but teams still double check results manually.
- How would you attribute a bad launch decision back to a Statsig data quality issue.
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More questions from OpenAI
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop