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Meta·Data Scientist·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Meta DS interview with a product analytics case about whether a specific feature is hurting engagement. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

Q1

A PM thinks the Re-share feature is hurting overall user engagement. How do you figure out if that's actually true, what metrics do you look at, and how do you design an experiment to test it?

A/B Testing & ExperimentationProduct Analytics & MetricsRoot Cause Analysis
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AI HintsAI Generated

Suggested Approach

Start by clarifying what 'overall user engagement' means and how Re-share might affect it, then use existing data to check for correlation and potential confounders. Design a rigorous A/B test that isolates the causal effect of Re-share on key engagement metrics, ensuring proper randomization, power, and guardrail metrics.

Pro tip: Frame the problem as a causal inference question: the PM's observation is an association, not causation. Propose a test that measures both the direct effect on sharers and the indirect effect on receivers, and consider heterogeneous treatment effects.

1. Clarify the hypothesis and define metrics

Work with the PM to specify what 'hurting engagement' means: which engagement metrics (e.g., DAU, time spent, sessions) and for which user segments. Define primary, secondary, and guardrail metrics.

2. Conduct exploratory analysis

Use observational data to check correlations between Re-share usage and engagement metrics, controlling for confounders like user activity level. Look for patterns that support or contradict the hypothesis.

3. Design the experiment

Propose an A/B test where users are randomized to either have Re-share available (control) or not (treatment). Ensure proper randomization unit (e.g., user-level), sufficient power, and duration to capture novelty effects.

4. Analyze results and validate

Compare primary and guardrail metrics between groups using appropriate statistical tests. Check for heterogeneous treatment effects and ensure results are robust (e.g., via segmentation, sensitivity analysis).

5. Interpret and recommend

Synthesize findings into a clear recommendation: if Re-share hurts engagement, suggest alternatives (e.g., redesign, limit); if not, advise keeping it. Highlight limitations and next steps.

Key Points to Mention

  • Causal inference vs. correlation: observational data alone cannot prove causation; need experiment.
  • Define engagement metrics clearly: e.g., DAU, time spent, sessions per user, content creation, retention.
  • Consider network effects: Re-share affects both sharers and receivers, so randomization might need to be at cluster level to avoid interference.
  • Guardrail metrics: ensure removing Re-share doesn't harm other important metrics like user satisfaction or content spread.
  • Statistical power and sample size: calculate required sample to detect meaningful effect.
  • Heterogeneous treatment effects: analyze by user segments (e.g., power users vs. casual) to see if effect varies.

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