Meta·Data Scientist·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Meta DS interview with a meaty experiment design question about group call participant caps. One question, but it had like five sub-parts and covered basically everything from randomization to stopping rules. Felt more like a take-home prompt delivered verbally.

Questions Asked (1)

You need to decide the maximum number of participants allowed per Group Call, choosing between caps of 4, 8, or 16. Design an experiment to make that decision. Walk through: your choice of fixed multi-arm vs adaptive design given a 4-week timeline, your primary metric and reliability guardrails, how you'd stratify randomization across device class, network type, and region, how you'd detect non-monotonic effects and segment differences, and your stopping rules including rollback thresholds if latency SLOs are breached.

A/B Testing & ExperimentationProduct Analytics & MetricsTechnical Trade-offs

Author's notes

This one sprawled.

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AI HintsAI Generated

Suggested Approach

Start by framing the decision as a trade-off between engagement benefits and technical costs, then propose a fixed multi-arm design with equal allocation to 4, 8, and 16 participants, given the short timeline. Emphasize guardrail metrics like latency and call quality, and outline a stratified randomization plan and analysis approach that detects non-monotonic effects and segment differences.

Pro tip: Predefine rollback thresholds and a kill switch for latency SLO breaches to protect user experience; this shows you prioritize reliability alongside growth.

1. Choose experimental design

Given the 4-week timeline, opt for a fixed multi-arm design with equal allocation to caps of 4, 8, and 16. This allows direct comparison of all options and avoids the complexity of adaptive designs that require more time to converge.

2. Define metrics and guardrails

Primary metric: call success rate or average call duration (engagement). Guardrails: join latency, audio/video quality (e.g., MOS), and crash rate. Set thresholds for acceptable degradation.

3. Stratify randomization

Randomize at the user level, stratified by device class (high/low), network type (WiFi/cellular), and region (NA/EU/APAC) to ensure balanced representation and enable segment analysis.

4. Analyze non-monotonic effects and segments

Use regression with polynomial terms or compare each arm to control to detect non-linear trends. Conduct subgroup analyses for device, network, and region, with corrections for multiple comparisons.

5. Set stopping rules and rollback

Monitor guardrails daily; if latency SLO breach exceeds threshold (e.g., >5% degradation) in any arm, pause that arm. Use sequential testing or predefine a fixed sample size to avoid peeking. Rollback if severe issues.

Key Points to Mention

Fixed multi-arm design is efficient for short timelines; adaptive designs need more time.
Primary metric should reflect call success/engagement; guardrails include latency, quality, and crashes.
Stratification ensures balanced groups and enables segment-level insights.
Non-monotonic effects can be detected via polynomial contrasts or pairwise comparisons.
Stopping rules: predefine thresholds for guardrail breaches and use sequential testing if monitoring continuously.
Rollback plan: immediate rollback if latency SLO breached beyond acceptable threshold.

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