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Meta·Data Scientist·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Meta DS interview with a product-flavored experiment design question about feed ranking. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

Q1

You have evidence that users are more socially active when engaging with posts from people they know. Would you still increase the share of posts from strangers in their feed? Walk through how you'd evaluate and make that call.

A/B Testing & ExperimentationProduct Analytics & MetricsProduct Strategy
Author's notes

I spent the first two minutes just restating the problem back, which felt like stalling but actually helped me get my footing.

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AI HintsAI Generated

Suggested Approach

Acknowledge the evidence but reframe the decision as a trade-off between short-term engagement and long-term ecosystem health. Propose a structured evaluation using metrics beyond social activity, such as content diversity, user retention, and creator ecosystem balance. Recommend running a controlled experiment to measure net impact before making a final call.

Pro tip: Emphasize that at Meta, decisions are made with a long-term user value lens, not just immediate engagement. Show you understand that increasing stranger content might boost short-term metrics but could harm user well-being and retention if not carefully balanced.

1. Clarify the goal and constraints

Define what 'socially active' means and what business objective the increase in stranger posts aims to achieve. Consider platform values like meaningful social interactions and user well-being.

2. Identify potential benefits and risks

List pros (e.g., discovery, new connections, content diversity) and cons (e.g., reduced social interaction, echo chambers, user dissatisfaction). Map these to short-term and long-term metrics.

3. Design an experiment with holistic metrics

Propose an A/B test with a treatment group seeing more stranger posts. Define success metrics including engagement (likes, comments, shares), retention, content diversity, and user sentiment.

4. Analyze trade-offs and segment impact

Evaluate results across user segments (e.g., new vs. existing users, heavy vs. light socializers). Look for heterogeneous treatment effects and net impact on overall ecosystem health.

5. Make a recommendation with guardrails

Decide whether to proceed, and if so, under what conditions (e.g., cap on stranger posts, personalization). Suggest ongoing monitoring and iteration.

Key Points to Mention

  • Trade-off between short-term engagement and long-term user retention/well-being
  • Importance of defining clear success metrics beyond social activity (e.g., meaningful interactions, content diversity)
  • Use of A/B testing to measure causal impact and avoid confounding
  • Segmentation analysis to understand heterogeneous effects across user groups
  • Consideration of platform ecosystem health (creators, consumers, advertisers)
  • Ethical implications and potential for unintended consequences like echo chambers or misinformation

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