Openai·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Data science interview at OpenAI, one question that sounds deceptively simple until you're actually sitting there trying to answer it.

Questions Asked (1)

You shipped a new feature and user behavior shifted as a result. How do you define what success looks like?

Product Analytics & MetricsA/B Testing & ExperimentationProduct Sense & Ideation

Author's notes

I went straight to engagement metrics and the interviewer just kind of waited, like they wanted more.

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AI HintsAI Generated

Suggested Approach

Start by tying success to the feature's original goal and the specific user behavior shift you observed, then define a hierarchy of metrics (north star, guardrails, counter-metrics) with clear targets. Emphasize that success is not just the shift itself but whether it drives durable, positive outcomes for users and the business.

Pro tip: Anchor your answer in a real or hypothetical example, and explicitly call out how you'd avoid vanity metrics and false positives by setting a pre-registered decision rule before the experiment.

1. Revisit the feature's goal

Restate the original problem and hypothesis: what user need were you solving, and what outcome did you expect? This grounds success in intent, not just observed change.

2. Define the primary success metric

Choose one north-star metric that directly reflects the desired user behavior shift (e.g., engagement, retention, task completion) and set a target based on baseline or experiment design.

3. Add guardrail and counter-metrics

Identify metrics that must not degrade (e.g., latency, error rates, support tickets) and counter-metrics that could reveal unintended harm (e.g., decreased quality, increased churn).

4. Set evaluation criteria and timeframe

Specify the measurement window, statistical significance threshold, and decision rule (e.g., ship, iterate, rollback) before analyzing results to avoid post-hoc rationalization.

5. Validate with qualitative and long-term signals

Supplement quantitative metrics with user feedback, session replays, or cohort analysis to ensure the shift is meaningful and sustainable, not a short-term spike.

Key Points to Mention

North star metric tied to the feature's core value proposition
Guardrail metrics to catch regressions (performance, quality, safety)
Counter-metrics to detect unintended consequences (e.g., decreased diversity of content)
Pre-registered decision criteria and statistical significance
Segmentation by user cohort to understand who benefits or is harmed
Long-term retention or LTV as a check against short-term engagement gains

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