This is where I spent most of my time and also where I stumbled.
Start by defining the marketplace metrics that the ETA model could influence, such as rider wait time, match rate, and driver utilization. Then design a randomized controlled experiment (A/B test) with proper randomization, sample size, and guardrail metrics to measure the causal impact. Finally, analyze the results with consideration for network effects and long-term effects.
Pro tip: In marketplace experiments, interference between treatment and control units can bias results, so consider using switchback or cluster randomization to account for network effects. Also, pre-register your analysis plan to avoid p-hacking and ensure credibility.
Clearly state the hypothesis (e.g., new ETA model improves rider experience and marketplace efficiency) and select primary, secondary, and guardrail metrics (e.g., ETA accuracy, rider wait time, match rate, driver utilization, cancellations).
Choose randomization unit (e.g., rider, driver, or geographic region) and method (e.g., A/B test, switchback) to minimize interference. Determine sample size and duration based on power analysis, accounting for seasonality and day-of-week effects.
Run the experiment, ensuring proper implementation and monitoring for data quality, sample ratio mismatch (SRM), and early guardrail violations. Use holdout groups if needed.
Compare metrics between control and treatment using statistical tests, checking for significance and practical impact. Explore heterogeneous treatment effects and segment analysis.
Assess long-term effects through holdout or post-experiment analysis, and consider qualitative feedback. Decide whether to launch, iterate, or abandon based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business goal of the new ETA model—likely improving prediction accuracy to enhance marketplace efficiency and user experience. Then, structure your answer by defining metrics for both riders and drivers, ensuring they are actionable, measurable, and tied to the model's impact. Finally, emphasize the importance of guardrail metrics and long-term effects.
Pro tip: Highlight the need to balance rider and driver metrics to avoid optimizing one side at the expense of the other, and mention how you would use A/B testing to measure causal impact.
Confirm the primary goal of the new ETA model, such as reducing prediction error to improve reliability and trust. This sets the context for selecting relevant metrics.
Identify metrics that capture rider experience and behavior, such as ETA accuracy (e.g., mean absolute error), cancellation rate, wait time, and conversion rate. These reflect how well the model meets rider expectations.
Identify metrics that capture driver experience and efficiency, such as driver utilization, acceptance rate, time to pickup, and earnings per hour. These reflect how the model affects driver operations.
Consider overall marketplace health metrics like completed trips, match rate, and surge pricing frequency. Also include guardrails like app crashes or latency to ensure no negative side effects.
Select a primary metric (e.g., ETA accuracy) and secondary metrics, then design an A/B test to measure impact. Ensure metrics are sensitive to the model change and aligned with long-term goals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining what intermediate/leading indicators are and why they matter for early signal detection. Then, outline a framework for selecting and monitoring these metrics, emphasizing alignment with the experiment's goal and guardrail metrics. Conclude with how you would use these indicators to make informed decisions before final results.
Pro tip: Focus on metrics that are sensitive to the treatment and predictive of the final outcome, but be cautious of peeking and false positives; use sequential testing or Bayesian methods to allow early stopping without inflating error rates.
Determine the main success metric (e.g., conversion rate) and brainstorm upstream metrics that logically precede it (e.g., click-through rate, add-to-cart rate).
Choose metrics that ensure the experiment isn't causing harm, such as latency, error rates, or customer satisfaction, which should be monitored continuously.
Implement dashboards and alerts for these metrics to detect anomalies or early trends, ensuring data quality and proper logging.
Use techniques like sequential testing, Bayesian analysis, or CUPED to account for peeking and reduce variance, enabling valid interim analyses.
Establish thresholds for when to stop, continue, or modify the experiment based on leading indicators, and communicate these to stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is the one I wish I had more time to prep.
Start by clarifying the experimental context—whether randomization is feasible and if spillover or interference is a concern. Then, compare classic A/B testing and synthetic control on randomization unit, duration, and interpretation, ultimately recommending the approach that best balances validity and practicality for Uber's marketplace. Emphasize that the choice depends on the specific constraints and goals of the experiment.
Pro tip: Acknowledge that synthetic control is powerful for city-level or marketplace interventions where randomization is impossible, but highlight its limitations in measuring individual-level effects. Show you understand Uber's two-sided marketplace dynamics and the importance of avoiding interference between riders and drivers.
Ask about the intervention, available data, and whether randomization is possible. Identify if there are network effects or spillover risks that could violate A/B testing assumptions.
For A/B tests, discuss units like user, driver, trip, or city, and trade-offs (e.g., user-level avoids spillover but may miss marketplace effects; city-level captures interference but reduces power). For synthetic control, the unit is typically a geographic market or city.
For A/B tests, duration depends on power, novelty effects, and metric sensitivity; for synthetic control, need sufficient pre-period to build a reliable counterfactual and post-period to detect effects. Consider Uber's high-frequency data and weekly seasonality.
For A/B tests, use hypothesis testing and confidence intervals, check for SRM and interference. For synthetic control, assess pre-period fit, placebo tests, and effect size; be cautious about extrapolating to individual-level behavior.
Recommend classic A/B if randomization is feasible and interference is minimal; otherwise, synthetic control. Discuss how the choice impacts inference, generalizability, and business decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.