Pick a feature that addresses a clear pain point for either riders or drivers, and structure your answer around a coherent narrative: user problem, proposed solution, metrics, tradeoffs, and experiment design. Emphasize how you would measure success and mitigate marketplace interference, showing deep understanding of Uber's two-sided platform.
Pro tip: Acknowledge that Uber's marketplace is a two-sided network with spillovers; propose an experiment design that accounts for interference, such as cluster randomization or switchback testing, to demonstrate sophistication.
Choose a specific user segment (e.g., riders in low-density areas, drivers during peak hours) and articulate a clear, validated pain point. Use data or qualitative insights to justify why this problem matters.
Describe the feature and how it directly addresses the pain point. Explain the expected behavior change for users and how it benefits the platform (e.g., increased engagement, reduced cancellations).
Select a north-star metric that captures the core value of the feature (e.g., completed trips per rider, driver utilization). List supporting success metrics across rider experience, driver experience, safety, and revenue.
Discuss potential tradeoffs: how the feature might impact rider wait times, driver earnings, safety incidents, and revenue. Show balanced consideration of all stakeholders.
Outline an A/B test: randomization unit (e.g., rider, driver, city), duration, power analysis, and methods to handle marketplace spillovers (e.g., cluster randomization, switchback, or geo-based tests).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
My first instinct was seasonality, weather, more trips in summer, but the drop in November threw me.
Start by validating the metric's numerator and denominator to rule out data quality issues, then systematically test hypotheses about real safety changes versus reporting artifacts using internal and external data. Apply statistical and causal methods to isolate the root cause, considering seasonality, product changes, and external factors.
Pro tip: Always check if the spike aligns with known product or policy changes (e.g., new safety features, reporting incentives) before diving into external factors—often the simplest explanation is an internal artifact.
Verify the numerator (accidents) and denominator (completed trips) for data quality issues, definition changes, or reporting anomalies. Check for missing data, outliers, or shifts in data collection.
List potential causes: real safety changes (e.g., seasonal weather, driver behavior), reporting artifacts (e.g., increased reporting due to awareness campaigns), or product changes (e.g., new trip types, pricing).
Pull internal data on app usage, driver/rider demographics, safety feature adoption, and support tickets. Supplement with external data on weather, traffic, holidays, and local events.
Compare accident rates across similar cities or control groups, and analyze subpopulations (e.g., new vs. experienced drivers). Use time-series decomposition to isolate seasonality from trends.
Use regression with controls, difference-in-differences, or interrupted time series to test hypotheses. Consider instrumental variables or propensity score matching if confounding is present.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.