Chime·Data Scientist·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Chime Data Scientist interview that was basically one long experiment design question stretched across the whole session. Covers a lot of ground: metric selection, power analysis, pitfall detection, stakeholder comms. Dense but fair.

Questions Asked (1)

Walk through the full process of setting up, running, and analyzing an A/B test for a new recommendation widget, including how you'd handle pitfalls, determine sample size, pick primary metrics, and present results to stakeholders.

A/B Testing & ExperimentationProduct Analytics & MetricsStakeholder Management

Author's notes

This is a lot to cover in one question and I think I underestimated how much they wanted on the pitfall side.

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AI HintsAI Generated

Suggested Approach

Structure your answer around the end-to-end experimentation lifecycle: hypothesis, design, execution, analysis, and stakeholder communication. Emphasize how you would proactively address common pitfalls like sample ratio mismatch, novelty effects, and multiple testing, and tie your choices back to Chime's business goals such as increasing engagement or conversion.

Pro tip: Show that you think like an owner: before diving into statistics, clarify the business objective and how the test results will drive a decision. This demonstrates product sense and ensures your analysis is actionable.

1. Define hypothesis and success metrics

Start with a clear, testable hypothesis (e.g., 'The new recommendation widget will increase click-through rate by 5%'). Choose a primary metric (e.g., CTR) and guardrail metrics (e.g., page load time, revenue per user) that align with business goals.

2. Design the experiment

Determine sample size using power analysis (80% power, 5% significance, minimum detectable effect). Randomize users into control and treatment groups, ensuring proper assignment and avoiding contamination. Decide on test duration to capture full weekly cycles and avoid novelty effects.

3. Run and monitor the test

Launch the test and monitor for data quality issues like sample ratio mismatch (SRM), instrumentation bugs, or unexpected external factors. Avoid peeking at results prematurely to prevent inflated false positive rates.

4. Analyze results

After the test concludes, perform statistical analysis: compare primary and guardrail metrics, check for significance, and compute confidence intervals. Investigate segment-level effects and consider practical significance, not just statistical significance.

5. Communicate and decide

Present findings to stakeholders with a clear recommendation (ship, iterate, or kill). Use visualizations and plain language to explain impact, uncertainty, and next steps. Tie results back to business KPIs.

Key Points to Mention

Sample size calculation: power, significance level, MDE, and baseline conversion rate.
Common pitfalls: SRM, novelty effect, multiple comparisons, peeking, and Simpson's paradox.
Primary metric selection: aligned with business goal, sensitive to change, and measurable.
Guardrail metrics: ensure no negative impact on user experience or other key metrics.
Stakeholder communication: tailor to audience, focus on actionable insights, and acknowledge limitations.
Iterative testing: consider sequential testing or Bayesian methods if appropriate.

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