Design an Effective A/B Test for Algorithm Launch

Quick Overview

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design an Effective A/B Test for Algorithm Launch states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Design an Effective A/B Test for Algorithm Launch

Company: Chime

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

##### Scenario A mobile app team wants to roll out a new recommendation algorithm and needs an A/B test to decide whether to launch it. ##### Question Describe end-to-end how you would design and run this A/B test. What metrics would you track and how would you define success? How do you determine the required sample size and test duration? Name common pitfalls in A/B testing and how to avoid them. ##### Hints Discuss randomization, segmentation, statistical power, guard-rail metrics, stopping rules, and launch criteria.

Overview: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design an Effective A/B Test for Algorithm Launch states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Aug 4, 2025
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Design an Effective A/B Test for Algorithm Launch

Design an A/B Test for a New Mobile App Recommendation Algorithm

Context

A mobile app team plans to ship a new recommendation algorithm that ranks content in the app. Assume:

  • We can randomize at the user level with sticky assignment.
  • Traffic is sufficient to run a 50/50 A/B split after a brief ramp.
  • The algorithm may change engagement and performance (e.g., latency).

Task

Describe, end-to-end, how you would design and run this A/B test:

  1. Experiment design
    • Unit of randomization, assignment, segmentation, and ramp plan.
    • Exposure definition and logging/instrumentation.
    • Analysis plan and stopping rules.
  2. Metrics and success criteria
    • Primary, secondary, and guard-rail metrics.
    • How you will define success and launch criteria.
  3. Sample size and test duration
    • How to determine required sample size and duration.
    • Include formulas and a small numeric example.
  4. Common pitfalls and mitigations
    • Discuss randomization, segmentation, statistical power, guard-rail metrics, stopping rules, and launch criteria.

Clarifying Questions to Ask Guidance

  • Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
  • State assumptions about instrumentation, randomization, sample size, and data quality.
  • Separate descriptive analysis from causal claims.

What a Strong Answer Covers Guidance

  • A metric framework with primary, guardrail, and diagnostic metrics.
  • A credible analysis or experiment design with clear assumptions and bias checks.
  • SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
  • An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions Guidance

  • What sanity checks would you run before trusting the result?
  • How would you handle novelty effects, seasonality, or selection bias?
  • What decision would you make if metrics disagree?
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