Choose group-call size cap via experiment

Quick Overview

This question evaluates a candidate's competency in experimental design, A/B testing and statistical analysis for product decisions, specifically testing metric selection, reliability and latency guardrails, stratified randomization, interference and clustering effects, heterogeneity detection, and operational stopping rules.

Choose group-call size cap via experiment

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Technical Screen

You must decide the maximum participants per Group Call. Propose an experiment to select the cap among {4, 8, 16}. Cover: (a) Design choice (fixed multi-arm vs bandit) under a 4-week deadline; justify exploration vs exploitation trade-offs. (b) Primary metric (e.g., successful starts per eligible user) and reliability/latency guardrails (start success rate, join latency p95/p99, crash rate). (c) Stratified randomization to balance device class, network type, and region; how to handle cluster/household effects. (d) Detect non-monotonic effects (8 may outperform 4 and 16) and heterogeneity by segment (friends vs workgroups); specify an analysis that controls false discovery (e.g., hierarchical modeling or Holm-Bonferroni). (e) Stopping rules, interim looks, and rollback thresholds if tail latency SLOs are breached; include how you would cap exposure to protect reliability while still making a decision.

Quick Answer: This question evaluates a candidate's competency in experimental design, A/B testing and statistical analysis for product decisions, specifically testing metric selection, reliability and latency guardrails, stratified randomization, interference and clustering effects, heterogeneity detection, and operational stopping rules.

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Oct 13, 2025, 9:49 PM
hardData ScientistTechnical ScreenAnalytics & Experimentation
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Decide the Maximum Participants per Group Call: Experiment Plan

Context: You need to choose a default cap for group calls (maximum concurrent participants) among {4, 8, 16}. The decision must be made within 4 weeks and must protect call reliability and latency. Assume 8 is the current production default unless stated otherwise.

Specify an experiment that covers:

(a) Design choice: fixed multi-arm vs. bandit under a 4-week deadline. Justify exploration vs. exploitation trade-offs.

(b) Metrics: choose a primary business metric (e.g., successful starts per eligible user) and define reliability/latency guardrails (e.g., start success rate, join latency p95/p99, crash rate), including thresholds.

(c) Randomization: stratified randomization to balance device class, network type, and region. Explain how you will handle cluster/household effects and interference (participants from different arms joining the same call).

(d) Analysis: detect non-monotonic effects (8 could outperform both 4 and 16) and heterogeneity by segment (e.g., friends vs. workgroups). Outline an analysis that controls false discovery (e.g., hierarchical modeling or Holm–Bonferroni).

(e) Operations: stopping rules, interim looks, and rollback thresholds if tail latency SLOs are breached. Include how you will cap exposure to protect reliability while still enabling a decision in 4 weeks.

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