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Design and justify unread-accounts pinning experiment

Last updated: Mar 29, 2026

Quick Overview

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.

  • hard
  • Meta
  • Analytics & Experimentation
  • Data Scientist

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.

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Meta
Oct 13, 2025, 9:49 PM
Data Scientist
Technical Screen
Analytics & Experimentation
2
0

Experiment Design: Pin Unread Accounts at Top of Account Switcher

Context

You propose a feature for users who own multiple accounts (same person_id): when they open the account switcher, any accounts with UNREAD notifications are pinned to the top. The goal is to decide whether to launch this feature based on a rigorous experiment and decision framework.

Assume:

  • The feature affects only multi-account users (≥2 accounts per person_id).
  • A person can use multiple devices and platforms (web/iOS/Android).
  • The account switcher lists all accounts associated with the same person_id.

Tasks

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.

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