This one took me a while to even understand what they were really asking.
Start by defining the rider experience in terms of supply-demand dynamics: availability, reliability, and efficiency. Then propose metrics for each dimension, ensuring they are measurable and actionable. Finally, design a composite index with clear weights, justifying them based on business priorities and rider sensitivity.
Pro tip: When proposing weights, anchor them to a known business objective (e.g., reducing wait time) and mention that weights can be tuned via rider surveys or A/B tests to reflect actual rider preferences.
Identify the core aspects of rider experience influenced by supply-demand: availability (can riders get a ride?), reliability (is the ride on time?), and efficiency (how quickly and cheaply?).
For each dimension, select 1-2 metrics that are directly tied to supply-demand. For example, availability: match rate; reliability: pickup ETA and cancellation rate; efficiency: surge multiplier and wait time.
Normalize each metric to a 0-1 scale (e.g., using min-max or percentile) and assign weights based on importance. Weights can be derived from rider surveys or business KPIs.
Combine weighted normalized metrics into a single index. For example: Rider Experience Index = w1*MatchRate + w2*(1 - PickupETA_norm) + w3*(1 - CancellationRate) + w4*(1 - SurgeMultiplier_norm).
Validate the index by correlating with rider satisfaction scores or retention. Iterate on weights and metrics based on feedback and changing business goals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Censoring came up as a follow-up and I was actually more comfortable here.
Start by clarifying the business context and the specific metric (e.g., unfulfilled ride rate) and its impact on user experience and marketplace health. Then, outline a principled approach to defining censoring rules that distinguish between true demand-supply mismatches and data quality issues, and explain how to handle exclusions like holidays or outages through robust data flags and sensitivity analyses.
Pro tip: Emphasize that censoring rules should be defined before analysis to avoid bias, and always quantify the impact of exclusions by comparing metrics with and without them. This shows rigor and prevents stakeholders from questioning the validity of your results.
Define what 'unfulfilled ride requests' means (e.g., no driver found, rider canceled, etc.) and why it matters for Uber's marketplace. Discuss how censoring rules affect metric interpretation and business decisions.
Establish rules to exclude requests that are not valid measures of unmet demand, such as requests from fraudulent accounts, test accounts, or those canceled by riders within a short window. Consider time-based censoring (e.g., requests older than X minutes) to focus on actionable failures.
Create flags for holidays, platform outages, and other anomalies using historical data or external calendars. Decide whether to exclude these periods entirely or adjust metrics (e.g., using robust statistical methods) to account for them.
Apply censoring rules in data pipelines, ensuring they are reproducible and documented. Monitor the impact of exclusions on key metrics and set up alerts for unexpected changes.
Perform sensitivity analyses to show how exclusions affect conclusions. Communicate the rationale and impact to stakeholders, and be transparent about limitations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the metric suite and the expected behavior under supply/demand shocks versus UI-only changes. Then outline a backtesting plan using historical data and synthetic experiments to validate sensitivity and stability, emphasizing statistical rigor and practical constraints.
Pro tip: Use real historical shocks (e.g., weather events, promotions) as natural experiments to test sensitivity, and leverage A/A tests or placebo interventions to check stability. This demonstrates you can work with messy real-world data while maintaining scientific rigor.
Clearly specify which metrics should respond to supply/demand shocks (e.g., completed trips, ETAs) and which should remain stable during UI-only changes (e.g., conversion rate, session length).
Gather historical data on supply/demand shocks (e.g., surge pricing, driver strikes, weather) and UI-only changes (e.g., button color, layout) to use as test cases.
For shocks, compare metric values before/during/after using statistical tests (e.g., t-test, CUSUM) to confirm significant changes. For UI changes, use A/A tests or placebo tests to ensure metrics remain stable.
Generate synthetic data where you control supply/demand and UI effects to stress-test the metric suite under various scenarios, including edge cases.
Assess sensitivity (true positive rate for shocks) and specificity (true negative rate for UI changes), and refine metrics if they fail to meet criteria.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.