Design and justify unread-accounts pinning experiment
Company: Meta
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: hard
Interview Round: Technical Screen
You propose a feature that, for users who own multiple accounts (same person_id), pins any accounts with UNREAD notifications to the top of the account switcher. Your goal is to decide whether to launch.
Design a concrete experiment and decision framework, then answer the scenario below. Be specific and justify trade-offs:
1) State the primary hypothesis and the target population. Define the unit of randomization and why (e.g., person_id-level cluster vs. account-level), and how you will prevent cross-account contamination when a person has accounts in both variants.
2) Select one Overall Evaluation Criterion (OEC) and 3–5 secondary/diagnostic metrics. For each metric, define the exact numerator/denominator and attribution window (e.g., 'unread resolution rate within 24h', 'cross-account message send rate per person-day', 'account-switch CTR', 'median dwell time on switched-to account', 'notification overload complaints per 1k users', etc.). Explain why the OEC is preferable to simpler clicks/CTR metrics.
3) List 3+ guardrails (e.g., retention, session length, error rate, notification latency, sample ratio mismatch) and the thresholds that would trigger a halt.
4) Specify the exposure and analysis windows (e.g., 28-day run with a 7-day cooldown), novelty/learning effects handling, and how you will handle users who gain/lose accounts mid-experiment.
5) Outline your power analysis inputs and outputs: baseline(s), minimal detectable effect (MDE), variance reduction methods (e.g., CUPED or covariate adjustment), and expected sample size/time to reach 80% power at alpha=0.05. You may assume realistic baselines but must show the formulas or the exact inputs you would plug into a calculator.
6) Data quality and attribution: define 'visit', 'dwell time', 'unread resolved', and how you will attribute outcomes when users switch across web/mobile (cross-device identity). Describe logging you need to add.
7) Decision scenario (interpretation test): Suppose the A/B test shows +40% lift in account-switch CTR to pinned unread accounts, but (a) median dwell time on the switched-to account is <5 seconds, (b) no statistically significant lift in reply/send-message rate or 'unread resolved within 24h', and (c) a +3% increase in bounce-back to the previous account within 10 seconds. Is this a successful outcome under your OEC? Give a yes/no decision and defend it. Propose at least two next steps (e.g., change ranking logic, add richer previews, segment by notification type or user intent) and how you would test them.
Quick Answer: This question evaluates a data scientist's experimentation and product-analytics competencies, including randomized experiment design, unit-of-randomization and contamination handling, metric and OEC selection, power analysis, guardrails, and cross-device attribution.