Meta·Data Scientist·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026

Summary

Meta DS interview with a classic product metrics scenario. One question, but it had enough layers to keep me busy for the whole session.

Questions Asked (1)

Comments per post dropped sharply last week on Facebook Groups. Walk through how you'd investigate this, including what data cuts you'd look at, what hypotheses you'd form, what metrics you'd track, and what follow-up experiments you might run.

Root Cause AnalysisProduct Analytics & MetricsA/B Testing & Experimentation

Author's notes

I started with the obvious stuff (is this a logging issue, is it one region, one platform) and that part went fine.

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AI HintsAI Generated

Suggested Approach

Start by validating the metric drop and segmenting the data to localize the issue (e.g., by platform, region, group type). Then form hypotheses around potential causes (product changes, external events, data pipeline issues) and prioritize them based on impact and likelihood. Finally, propose experiments to test the most plausible hypotheses and monitor key metrics.

Pro tip: Always check for data quality issues first—sometimes a 'drop' is just a logging bug or pipeline failure. Also, consider seasonality and external events (e.g., holidays) that might affect user behavior.

1. Validate and Localize the Drop

Confirm the drop is real and not due to data issues. Segment the data by dimensions like platform, region, group size, and user demographics to identify where the drop is concentrated.

2. Form Hypotheses

Brainstorm potential causes: product changes (e.g., algorithm update, UI change), external factors (e.g., holidays, news events), or user behavior shifts. Prioritize hypotheses based on impact and likelihood.

3. Analyze Metrics and Funnel

Track related metrics such as post creation, comments per user, active groups, and engagement funnel steps. Compare pre- and post-drop periods to pinpoint where the funnel breaks.

4. Design Follow-up Experiments

Based on top hypotheses, propose A/B tests or quasi-experiments to isolate causes. For example, test a UI change or algorithm tweak to see impact on comments.

5. Monitor and Iterate

After implementing fixes, monitor metrics to ensure recovery. Continuously iterate and validate with further experiments if needed.

Key Points to Mention

Data quality checks: ensure logging, ETL pipelines, and metric definitions are consistent.
Segmentation: break down by platform (iOS/Android/Web), region, group type (public/private), and user activity level.
Hypothesis prioritization: use frameworks like impact vs. effort or likelihood vs. impact.
Funnel analysis: examine steps from post view to comment (e.g., impressions, clicks, comment box opens).
External factors: consider holidays, major events, or platform-wide changes.
Experimentation: design A/B tests with proper power analysis and guardrail metrics.

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