This one is harder than it looks because the fairness angle is a trap you can walk into without realizing.
Start by clarifying the feature's goal and defining a clear hypothesis, then outline a randomized controlled experiment with appropriate randomization unit and guardrail metrics. Structure your answer around the four metric dimensions (business, safety, marketplace, fairness) and discuss how multi-city testing introduces heterogeneity that requires stratified randomization, city-level fixed effects, and careful analysis of treatment effect variation.
Pro tip: Acknowledge that this feature could backfire by stigmatizing neighborhoods or causing drivers to avoid certain areas, so include fairness and marketplace health as guardrails, not just afterthoughts. Also, propose a pre-registered analysis plan to avoid p-hacking across cities.
Define the feature's intended effect (e.g., increase driver safety perception) and state a testable hypothesis. Identify the primary decision metric and minimum detectable effect.
Choose randomization unit (driver, trip, or city), treatment/control groups, and duration. Consider switchback or cluster randomization if spillover is a concern. Pre-register the analysis plan.
Define business (e.g., completed trips, driver retention), safety (e.g., incidents, driver reports), marketplace (e.g., ETAs, acceptance rates, cancellations), and fairness (e.g., differential impact on low-income or minority neighborhoods) metrics.
Stratify randomization by city, include city fixed effects, and test for treatment effect heterogeneity. Use hierarchical models or meta-analysis to pool results while accounting for city-level variance.
Check for statistical significance, practical significance, and guardrail violations. Investigate heterogeneous effects and potential unintended consequences before making a recommendation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt like a warmup but they pushed hard on the ML side.
Start by linking ETA accuracy to core business metrics like rider trust, conversion, and driver utilization, then define a metric framework that captures both bias and precision. For diagnosis, segment errors by time, location, and trip characteristics, and propose improvements across data, modeling, and product interventions.
Pro tip: Emphasize that ETA accuracy is not just about average error but about the tails—large errors disproportionately damage trust and retention. Also, consider the cost of over- vs under-prediction: under-prediction frustrates riders, while over-prediction may reduce conversion.
Explain why ETA accuracy matters: it drives rider trust, conversion, retention, and driver efficiency. Inaccurate ETAs lead to cancellations, poor ratings, and lost revenue.
Define metrics that capture both central tendency and tails: MAE, RMSE, bias (mean error), percentage of trips with error > X minutes, and correlation with actual wait times. Also track business metrics like cancellation rate and conversion.
Segment errors by time of day, geography, trip distance, traffic conditions, and driver/rider behavior. Use residual analysis and error decomposition to identify systematic biases vs. random noise.
Propose improvements: better data (real-time traffic, historical patterns), advanced modeling (ML with features like weather, events), and product changes (e.g., showing ranges, updating ETAs dynamically).
Set up A/B tests to measure impact of changes on both ETA accuracy and business metrics. Continuously monitor performance and iterate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by discussing the potential behavioral and marketplace effects of the promotion, then explain why a simple user-level A/B test is invalid due to interference and spillover. Next, analyze the switchback design's challenges, and finally outline how to estimate variance under a synthetic control approach, emphasizing the need for counterfactual construction and uncertainty quantification.
Pro tip: Demonstrate awareness that in marketplace experiments, the unit of randomization must align with the unit of interference; often this means randomizing at the market or time level, and using methods like switchback or synthetic control to handle spillovers.
Consider how the promotion could change driver behavior (e.g., increased online time, relocation to specific areas, gaming the system) and marketplace outcomes (e.g., supply-demand balance, rider wait times, prices). Also think about unintended consequences like cannibalization or inequitable distribution.
A simple user-level A/B test suffers from interference: treated drivers may affect control drivers through shared marketplace dynamics (e.g., competition for rides, surge pricing). This violates SUTVA and biases treatment effect estimates.
Switchback designs alternate treatment across time periods for the same unit (e.g., city). Issues include carryover effects (drivers may anticipate or remember promotions), time-varying confounders (e.g., weather, events), and reduced power due to temporal correlation. Also, the treatment is at the time level, so individual driver effects are not directly estimable.
Synthetic control constructs a counterfactual by weighting control units (e.g., other cities) to match the treated unit's pre-treatment characteristics. Variance estimation can be done via placebo tests (in-space and in-time), bootstrap, or by modeling the synthetic control's prediction error. Key is to account for uncertainty in the weights and the counterfactual.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.