The math itself is trivial but I almost tripped on the denominator.
Start by clarifying the business definition of 'completion rate' and the exact slice (city and order type). Then define the numerator as the count of orders with status 'completed' and the denominator as the count of all orders (or all non-cancelled orders) in that slice, and write a SQL query to compute the ratio.
Pro tip: Always confirm whether the denominator should include cancelled orders or only orders that were attempted; this choice can significantly change the metric and reflects product sense.
Ask what 'completion rate' means in this context: is it completed orders divided by all orders, or divided by orders that were not cancelled? Confirm the exact status values that count as 'completed'.
Numerator: count of orders with status = 'completed' for the given city and order type. Denominator: count of all orders for that city and order type (or a subset if specified).
Use a SELECT statement with a WHERE clause filtering city and order type, and compute SUM(CASE WHEN status = 'completed' THEN 1 ELSE 0 END) / COUNT(*) as the completion rate.
Check for NULLs, empty slices, and whether the denominator should exclude certain statuses. Validate the result with a quick sanity check (e.g., rate between 0 and 1).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I got the empty-slice case right away (return 0.0 or null, not a division error).
Start by clarifying the metric's definition, data sources, and business context to identify potential edge cases. Then systematically walk through the data pipeline—from data collection to aggregation—highlighting specific edge cases and how you would handle them. Emphasize the importance of robust validation and monitoring to catch these issues in production.
Pro tip: Tie each edge case to a real-world impact at Uber (e.g., driver incentives, surge pricing) to show business acumen. Also, mention that you'd prioritize edge cases based on frequency and severity, and implement automated tests to prevent regressions.
Ask questions to understand the exact definition, data sources, and how the metric is used. Identify the unit of analysis (e.g., per trip, per driver) and the time window.
Trace the data flow from collection to aggregation. Identify stages where edge cases can arise: data ingestion, cleaning, transformation, and computation.
List potential edge cases such as missing data, outliers, time zone issues, duplicate records, and boundary conditions (e.g., zero denominators).
For each edge case, suggest a mitigation: imputation, robust statistics, filtering, or business rules. Discuss trade-offs between accuracy and simplicity.
Describe how you would test the metric with synthetic edge cases and set up monitoring to detect anomalies in production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining what 'completion rate' means in the Uber context (e.g., completed trips / requested trips) and segment the metric by city, order type, time, and user cohorts to localize the drop. Then systematically investigate potential causes across the funnel—demand, supply, pricing, product, and external factors—using data and experiments to validate hypotheses.
Pro tip: Emphasize that you would first check for data pipeline or logging issues before jumping to business causes, as ML engineers often find that metric anomalies stem from instrumentation bugs or model deployment changes.
Clarify the exact definition of completion rate (e.g., completed trips / requested trips) and break it down by city, order type, time period, user demographics, and device to identify where the drop is concentrated.
Verify that the data pipeline is functioning correctly—check for logging errors, missing events, or changes in event definitions that could artificially lower the completion rate.
Map the user journey from request to completion and measure conversion at each step (e.g., request → match → pickup → drop-off) to pinpoint where users are abandoning.
Consider potential causes such as supply-demand imbalance, pricing changes, product bugs, competitor actions, or external events (weather, traffic). Use A/B tests, causal inference, or correlation analysis to validate.
Based on findings, propose actionable fixes (e.g., incentives, algorithm tweaks) and set up monitoring to track the impact and prevent future issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about courier-order matching improvements, ETA model calibration, and targeted supply incentives.
Start by clarifying what 'completion rate' means in the specific context (e.g., trip completion, delivery completion, or ML model training completion) and identify the key stages in the user journey where drop-off occurs. Then, propose a mix of product, operational, and ML changes that address the root causes, prioritizing based on impact and feasibility, and outline how you would measure success.
Pro tip: Anchor your answer in Uber's marketplace dynamics: completion rate is a two-sided problem, so consider both rider/driver and eater/courier perspectives. Also, mention that ML solutions should be deployed only after understanding the causal drivers, not just correlations.
Ask clarifying questions to understand which completion rate (e.g., trip, delivery, model training) and the current baseline. Define the numerator and denominator clearly.
Break down the funnel into stages (e.g., request, match, pickup, drop-off) and identify where drop-offs happen. Use data to find patterns (e.g., time of day, location, user segment).
Generate ideas across product (e.g., UI improvements, incentives), operational (e.g., driver supply, routing), and ML (e.g., better ETA prediction, dynamic pricing, fraud detection) categories.
Evaluate ideas by expected impact, effort, and risk. Suggest A/B tests or pilot programs to validate the most promising changes.
Define success metrics (e.g., completion rate lift, user satisfaction) and propose a monitoring plan to ensure sustained improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.