Shopify·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026Remote

Summary

Shopify data scientist interview with a single very deep experimentation question. The whole thing was basically one giant case study on A/B testing at scale, and it covered more ground than I expected in one sitting.

Questions Asked (1)

You're evaluating a core product change that likely has network effects in a large platform with millions of daily active users. Walk through your full experiment design: success metrics, randomization unit, test plan with ramp strategy and stopping rules, variance reduction techniques, sample size calculation for a specific scenario, how you'd detect heterogeneous treatment effects, what you'd do if randomization isn't feasible, and how you'd define ship vs rollback criteria and communicate results to stakeholders.

A/B Testing & ExperimentationProduct Analytics & MetricsStakeholder Management

Author's notes

This was one question but it kept expanding.

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AI HintsAI Generated

Suggested Approach

Structure your answer around the experiment lifecycle: define success metrics and randomization, design the test with ramp and stopping rules, apply variance reduction and sample size calculation, then address heterogeneity, non-randomization fallbacks, and ship/rollback criteria with stakeholder communication. Emphasize network effects and practical constraints at Shopify's scale.

Pro tip: For network-effect experiments, consider cluster randomization (e.g., by region or store) and measure spillover effects; also, pre-register your analysis plan to avoid p-hacking and build trust with stakeholders.

1. Define Success Metrics and Randomization Unit

Identify primary (e.g., conversion, revenue) and guardrail metrics (e.g., latency, support tickets). Choose randomization unit (user, session, or cluster) balancing network effects and statistical power.

2. Design Test Plan: Ramp, Stopping Rules, and Variance Reduction

Plan a gradual ramp (e.g., 1%, 5%, 20%, 50%) with predefined stopping rules (e.g., sequential testing, alpha spending). Apply variance reduction techniques like CUPED or stratification.

3. Calculate Sample Size and Detect Heterogeneous Effects

Compute sample size using power analysis for a specific scenario (e.g., 2% lift, 80% power, 5% significance). Plan subgroup analyses (e.g., by region, user tenure) with interaction tests or causal forests.

4. Handle Non-Randomization and Define Ship/Rollback Criteria

If randomization isn't feasible, use quasi-experimental methods (e.g., diff-in-diff, synthetic control, switchback). Define ship/rollback criteria based on primary metric lift, guardrail metrics, and business impact.

5. Communicate Results to Stakeholders

Tailor communication: for executives, focus on business impact and confidence; for engineers, on technical details. Use clear visuals and a decision recommendation.

Key Points to Mention

Network effects and spillover: use cluster randomization or measure interference.
Variance reduction: CUPED, stratification, or regression adjustment.
Sample size calculation: specify parameters (baseline, MDE, power, alpha) and account for clustering.
Heterogeneous treatment effects: pre-specified subgroups, interaction tests, or causal forests.
Non-randomization alternatives: switchback, diff-in-diff, synthetic control, or propensity score matching.
Ship/rollback criteria: pre-register thresholds for primary and guardrail metrics, consider long-term holdout.

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