Meta·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Meta DS interview for the Groups team. One meaty question covering both product health measurement and experiment design for a specific feature. Pretty representative of what to expect if you're going for a data science role there.

Questions Asked (1)

What metrics would you use to assess the current health of Facebook Groups, and how would you design an experiment to evaluate a new comment-collapsing feature before rolling it out?

Product Analytics & MetricsA/B Testing & ExperimentationProduct Sense & Ideation

Author's notes

Two questions stitched into one, which I didn't fully appreciate until I was already three minutes into talking about engagement metrics and the interviewer asked 'okay so what about the experiment.' I had to mentally restart.

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AI HintsAI Generated

Suggested Approach

Start by defining a balanced set of health metrics for Facebook Groups, covering engagement, retention, and community well-being. Then outline a rigorous A/B test design for the comment-collapsing feature, including hypothesis, randomization, success metrics, guardrails, and analysis plan. Emphasize how you would measure both intended and unintended consequences before a full rollout.

Pro tip: Show that you understand trade-offs: collapsing comments might increase civility but reduce deep discussions. Propose guardrail metrics like 'time spent reading comments' and 'replies per post' to catch negative effects early.

1. Define Group Health Metrics

Identify key dimensions of group health: engagement (DAU/MAU, posts, comments, reactions), retention (member retention, admin retention), and community well-being (reports, toxicity, meaningful interactions). Use a mix of quantitative and qualitative signals.

2. Prioritize Metrics with a North Star

Select a North Star metric (e.g., 'weekly active engaged members') and supporting metrics that align with Meta's goals for Groups. Explain how you'd balance growth with quality.

3. Formulate Experiment Hypothesis

State a clear hypothesis: e.g., 'Collapsing comments will reduce perceived toxicity and increase overall engagement by making threads less overwhelming.' Define the target population and randomization unit (e.g., users or groups).

4. Design A/B Test and Metrics

Choose primary success metrics (e.g., comment collapse rate, overall engagement, toxicity reports) and guardrail metrics (e.g., time spent, replies per post, user satisfaction). Determine sample size, duration, and statistical power.

5. Analyze and Decide Rollout

Plan for heterogeneous treatment effects (e.g., by group size or topic), monitor guardrails, and use sequential testing if needed. Recommend rollout only if success metrics improve without harming guardrails.

Key Points to Mention

Use a combination of engagement, retention, and well-being metrics to avoid optimizing for a single dimension.
Define a clear North Star metric and ensure it aligns with long-term group health.
Include guardrail metrics to detect negative side effects like reduced deep discussions or increased lurking.
Consider network effects and interference: randomize at the group level if spillover is likely.
Plan for heterogeneous treatment effects: the feature may work differently for large vs. small groups or different topics.
Use a phased rollout (e.g., 1% -> 5% -> 50%) with continuous monitoring to mitigate risk.

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