This is a broad question and I kind of froze deciding where to start.
Structure your answer as a clear end-to-end experiment lifecycle: hypothesis, metric selection, power analysis, execution, and analysis. Emphasize Chime-specific considerations like member trust, regulatory constraints, and mobile engagement metrics. Balance statistical rigor with practical product constraints.
Pro tip: Mention that you'd pre-register the analysis plan and guardrail metrics to prevent p-hacking and ensure the test doesn't harm member experience. Also, highlight the importance of checking for novelty effects and segmenting by user tenure, as new users may react differently to recommendations.
State a clear, testable hypothesis (e.g., new algorithm increases click-through rate on recommendations). Define primary metric (e.g., CTR), secondary metrics (e.g., conversion, engagement time), and guardrail metrics (e.g., member satisfaction, complaint rate).
Choose randomization unit (e.g., user-level), determine minimum detectable effect (MDE) based on business impact, and calculate sample size using power analysis (alpha=0.05, power=0.8). Estimate test duration based on daily traffic and required sample.
Set up A/B test with proper logging and QA. Monitor for data quality, sample ratio mismatch (SRM), and early guardrail violations. Ensure no peeking that would inflate false positives.
After test concludes, analyze primary and secondary metrics using appropriate statistical tests (e.g., t-test, bootstrapping). Check for heterogeneity across segments. Decide to ship, iterate, or kill based on statistical and practical significance.
Document learnings, including pitfalls encountered. If successful, plan for gradual rollout and long-term monitoring. If not, use insights to inform next hypothesis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.