This was the core question and it sprawled in every direction.
Start by defining the experiment's goal, primary metric, and guardrail metrics, then outline the randomization unit and sample size calculation. Describe the experimental design, including control and treatment groups, and how you would analyze the results to determine if M1 significantly outperforms M0. Finally, discuss potential pitfalls and how to mitigate them.
Pro tip: Emphasize the importance of choosing the right randomization unit (e.g., user or session) to avoid interference and ensure valid inference, and mention the need for a sufficiently long experiment to capture delayed effects like redemptions.
Clearly state the null and alternative hypotheses. Identify the primary metric (e.g., redemption rate) and guardrail metrics (e.g., user retention, revenue) to ensure the new model doesn't harm other aspects.
Choose the randomization unit (e.g., user ID) and determine the sample size using power analysis. Randomly assign users to control (M0) and treatment (M1) groups, ensuring balance on key covariates.
Launch the experiment, ensuring proper logging and data collection. Monitor for any technical issues or sample ratio mismatch (SRM) that could invalidate results.
Compare the primary metric between groups using appropriate statistical tests (e.g., t-test or bootstrap). Check for statistical significance and practical significance, and analyze guardrail metrics.
Conclude whether M1 outperforms M0 based on the primary metric without harming guardrails. Consider segment-level analyses and potential long-term effects before making a recommendation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with incremental profit per eligible user, which is redeem uplift times expected GMV minus the promo cost.
Start by clarifying the experiment's goal and the specific product change being tested, then propose a primary metric that directly measures that goal and is sensitive to the change. Follow with a set of guardrail metrics that capture potential negative side effects on user experience, business health, and long-term value.
Pro tip: Emphasize that the primary metric should be a leading indicator of long-term value, not just a short-term proxy, and that guardrails should include both user-centric and business-centric metrics to catch unintended consequences.
Ask questions to understand the specific product change, target audience, and the business objective (e.g., increase rider engagement, improve driver utilization). This ensures the metric aligns with the intended outcome.
Choose a single metric that directly measures the experiment's goal, is sensitive to the change, and is a leading indicator of long-term value. For example, if testing a new rider incentive, the primary metric could be completed trips per rider.
Brainstorm ways the change could harm other aspects of the user experience or business, such as increased cancellations, lower driver earnings, or reduced safety.
Choose metrics that monitor these potential harms, covering user experience (e.g., cancellation rate, support tickets), business health (e.g., revenue per trip, driver retention), and long-term value (e.g., rider retention).
Define acceptable thresholds for guardrail metrics (e.g., no more than 2% degradation) and explain how you would monitor them during the experiment to make data-driven decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric and test design (two-proportion z-test, equal allocation). Then compute the required sample size per variant using the standard formula for proportions, plugging in baseline p1=0.03, p2=0.035, alpha=0.05 two-sided, and power=0.80. Finally, interpret the result in practical terms (total sample, duration, and sensitivity to assumptions).
Pro tip: Mention that the sample size is driven by the smaller of the two proportions (baseline) and that you'd validate with a quick simulation or power analysis tool to account for any design nuances (e.g., unequal allocation, clustering). Also note that the MDE is absolute (0.5pp), not relative.
Confirm the metric is a proportion (redemption rate), the test is two-sided, and allocation is equal. State the null and alternative hypotheses.
Use the standard sample size formula for comparing two proportions: n per group = (Z_{α/2} + Z_β)^2 * (p1(1-p1) + p2(1-p2)) / (p2 - p1)^2, where p1=0.03, p2=0.035.
Calculate Z_{α/2}=1.96, Z_β=0.84, p1=0.03, p2=0.035. Compute the numerator and denominator to get n per group.
Round up to the nearest integer, multiply by number of variants for total sample. Discuss practical considerations like traffic, duration, and potential adjustments for unequal allocation or multiple comparisons.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through SRM checks first, then CUPED using pre-period outcomes as the covariate.
Start by outlining a rigorous experimental design that includes randomization, sample size calculation, and choice of analysis method (e.g., regression adjustment, CUPED). Then explain how you would reduce variance using techniques like stratification, covariate adjustment, or CUPED, and how you would handle repeated looks with sequential testing or alpha-spending to control false positives.
Pro tip: Emphasize that variance reduction and sequential testing are not just statistical fixes but also require cross-functional alignment with product and engineering teams to ensure proper implementation and interpretation.
Clearly state the null and alternative hypotheses, primary and guardrail metrics, and the unit of randomization. This sets the foundation for choosing the right analysis approach.
Select a method that balances power and simplicity, such as regression adjustment with pre-experiment covariates or CUPED, to increase sensitivity without introducing bias.
Apply techniques like stratification, covariate adjustment, or CUPED to reduce variance and increase the chance of detecting a true effect. Explain how these methods work and their assumptions.
Use sequential testing methods (e.g., group sequential boundaries, alpha-spending functions) or always-valid inference to control the overall false positive rate when monitoring results over time.
Check for SRM, ensure assumptions are met, and interpret the effect size with confidence intervals. Discuss practical significance and potential trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that interference and saturation are common in marketplace experiments like Uber's, then explain how you would diagnose and mitigate them using techniques like cluster randomization or switchback designs. Emphasize the trade-offs between bias reduction and practical constraints, and propose alternative designs with clear reasoning.
Pro tip: Show that you understand Uber's two-sided marketplace dynamics: interference often arises from supply-demand interactions, so consider designs that account for both riders and drivers, such as bipartite randomization or time-based switchbacks.
Identify symptoms like spillover effects, network effects, or resource competition. Use metrics such as treatment effect heterogeneity across geographies or time to detect interference.
Apply techniques like cluster randomization (e.g., by city or driver), switchback designs, or budget splitting to reduce interference. Consider using holdout groups or synthetic controls.
Discuss how each design affects statistical power, bias, and operational feasibility. For example, cluster randomization reduces interference but increases variance.
Suggest alternatives like switchback, cluster randomized, or bipartite experiments. Explain when each is appropriate and how they address saturation.
Recommend running A/A tests or simulations to validate design assumptions. Be prepared to adapt based on results and business constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Practical ops question that I wasn't expecting to go this deep on.
Start by framing the goal: maintaining valid comparisons by ensuring balanced assignment and minimizing interference. Then walk through mechanisms for budget parity (e.g., sequential testing, balanced allocation), throttling (e.g., rate limiting, traffic shaping), suppression lists (e.g., exclusion criteria, holdouts), and spillover (e.g., cluster randomization, geo-based experiments). Emphasize trade-offs and practical implementation at scale.
Pro tip: Highlight that perfect parity is often impossible; instead, focus on detecting and correcting imbalances via monitoring and using techniques like CUPED or stratified randomization to increase sensitivity. Also, mention that spillover can be mitigated by using switchback or cluster designs, which are common at Uber.
Clarify what budget parity means in context (e.g., equal sample sizes, equal exposure, or equal spend) and identify potential sources of imbalance and interference.
Use randomization units (user, session, geo) and techniques like stratified randomization or sequential testing to maintain balance as data accumulates.
Apply throttling to control traffic volume and avoid overload; use suppression lists to exclude users (e.g., those already exposed, employees) and maintain clean comparisons.
Choose appropriate randomization units (e.g., clusters, geos) and consider switchback designs to reduce contamination between arms.
Continuously monitor key metrics for imbalance and interference; use statistical methods to detect and correct issues, and be prepared to restart or adjust the experiment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.