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Meta·Data Scientist·Technical Phone Screen·Senior

Senior
May 2026

Summary

Meta DS interview with a product analytics case comparing two features across platforms. Pretty focused session, just the one meaty scenario but it had enough layers to keep you busy for a while.

Questions Asked (1)

Q1

How would you quantitatively compare the success of Instagram Stories versus Facebook Stories, and what metrics and methods would you use to do it rigorously?

A/B Testing & ExperimentationProduct Analytics & Metrics
Author's notes

I started rattling off metrics which felt okay at first, DAU posting rate, views per story, completion rate, time spent.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the comparison goal—are we evaluating engagement, retention, or business impact? Then propose a rigorous framework that aligns metrics with objectives, uses causal inference methods like A/B testing or quasi-experiments, and accounts for platform differences and network effects.

Pro tip: Acknowledge that direct comparison is tricky due to different user bases and contexts; suggest normalizing metrics or using propensity score matching to create comparable groups, showing you understand real-world constraints.

1. Define Success Criteria

Clarify what 'success' means for each product (e.g., user engagement, retention, ad revenue) and align with Meta's strategic goals. Establish primary and secondary metrics.

2. Select Metrics

Choose quantitative metrics such as DAU/MAU, time spent per user, stories created/viewed per user, completion rate, and retention. Ensure metrics are comparable across platforms.

3. Design Experiment or Quasi-Experiment

If possible, run an A/B test by randomly assigning users to see one format over the other. If not, use quasi-experimental methods like difference-in-differences or propensity score matching to control for confounders.

4. Analyze and Validate

Apply statistical tests (e.g., t-tests, bootstrapping) to compare metrics, check for significance, and validate with sensitivity analyses. Consider segment-level differences.

5. Interpret and Recommend

Synthesize findings into actionable insights, acknowledging limitations and suggesting next steps for product improvement or further research.

Key Points to Mention

  • Normalization of metrics (e.g., per-user averages) to account for different user base sizes
  • Use of causal inference methods (A/B testing, quasi-experiments) to establish causality
  • Consideration of network effects and platform-specific user behavior
  • Inclusion of both engagement and business metrics (e.g., ad revenue, retention)
  • Statistical rigor: power analysis, confidence intervals, multiple testing correction
  • Segment analysis (e.g., by demographics, geography) to uncover heterogeneous effects

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