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Snapchat·Data Scientist·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026Remote

Summary

Snapchat data scientist interview that went deep on A/B testing, and I mean really deep. One question, but it sprawled into six or seven distinct sub-problems covering everything from sample size math to bot filtering. Left feeling like I'd half-answered a take-home assignment out loud.

Questions Asked (1)

Q1

Walk through an end-to-end A/B test for adding a home-page banner: randomization unit, metric selection, sample size calculation, analysis approach, diagnostic checks, and launch decision criteria.

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

This started as one question and quietly became six.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a clear, step-by-step walkthrough of the experiment lifecycle, from defining the hypothesis and randomization unit to making a launch decision. Emphasize practical considerations like guardrail metrics, novelty effects, and Snapchat-specific nuances such as its unique user base and engagement patterns.

Pro tip: Always mention guardrail metrics and the importance of checking for sample ratio mismatch (SRM) before analyzing results—this shows you understand common pitfalls that can invalidate experiments. Also, tie your metrics to Snapchat's key objectives like daily active users (DAU) and time spent.

1. Define Hypothesis and Randomization Unit

Start with a clear hypothesis (e.g., 'Adding a home-page banner will increase user engagement'). Choose the randomization unit—typically user-level for Snapchat to avoid interference—and ensure it aligns with the metric's unit of analysis.

2. Select Metrics and Calculate Sample Size

Identify primary metric (e.g., DAU or time spent) and secondary/guardrail metrics (e.g., retention, revenue, app crashes). Perform power analysis to determine required sample size, considering baseline rates, minimum detectable effect (MDE), significance level (α), and power (1-β).

3. Run Experiment and Monitor Diagnostics

Launch the test, ensuring proper randomization and tracking. Monitor for SRM, novelty effects, and data quality issues. Use sequential testing or fixed-horizon analysis as appropriate, and check guardrail metrics for any negative impact.

4. Analyze Results and Make Launch Decision

After the test reaches required sample size, analyze primary and secondary metrics using appropriate statistical tests (e.g., t-test, bootstrap). Consider practical significance, segment-level effects, and long-term impact. Decide to launch, iterate, or stop based on pre-defined criteria.

Key Points to Mention

  • Randomization unit: user-level to prevent contamination and network effects.
  • Metric selection: primary metric tied to business goal (e.g., DAU), guardrail metrics to detect harm.
  • Sample size calculation: power analysis with baseline, MDE, α, and power; account for multiple testing if needed.
  • Analysis approach: intention-to-treat, use of CUPED or other variance reduction techniques, and handling of outliers.
  • Diagnostic checks: sample ratio mismatch (SRM), novelty/primacy effects, and data quality checks.
  • Launch decision criteria: statistical significance, practical significance, guardrail metrics, and potential for long-term impact.

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