I started with the obvious stuff like ID verification failures and age restrictions, then got into things like driver refusals and stricter delivery windows.
Start by clarifying the metric and segmenting the data to isolate the gap, then systematically brainstorm hypotheses across the funnel (discovery, checkout, delivery, post-delivery) and prioritize them by likely impact and testability. Conclude by suggesting how you'd validate the top hypotheses with data and experiments.
Pro tip: Frame your answer around the user journey and explicitly separate supply-side (driver/restaurant) from demand-side (customer) factors—this shows you think like a product-minded ML engineer who understands the business context, not just the model.
Define what 'completion rate' means (order placed vs. delivered) and check if the gap persists across cities, time of day, order value, and customer tenure. This prevents overgeneralizing and guides hypothesis generation.
Break the process into stages: browsing, cart, checkout (age verification, ID scan), payment, restaurant acceptance, driver assignment, delivery (ID check at door), and post-delivery. Identify where alcohol orders might drop off disproportionately.
Brainstorm plausible reasons across regulatory, operational, customer behavior, and supply-side dimensions—e.g., legal restrictions, driver reluctance, customer impatience with ID checks, or restaurant inventory issues.
Rank hypotheses by expected impact and ease of testing, then outline how you'd validate each with data (e.g., funnel analysis, A/B tests, driver surveys) and what metrics you'd track.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the hypotheses you've formed about why alcohol orders have lower completion rates (e.g., age verification friction, delivery restrictions, customer cancellations). Then, for each hypothesis, propose specific additional data sources and metrics that would confirm or refute it, prioritizing data that is readily available and actionable. Structure your answer around a test-and-learn framework, showing how you'd use data to narrow down root causes.
Pro tip: Emphasize the importance of segmenting the analysis by geography, time, and customer demographics, since alcohol regulations and behaviors vary widely—this shows you understand the operational complexity at Uber.
Restate the hypotheses (e.g., ID verification failures, restricted delivery zones, customer cancellations) and define what 'completion rate' means (order placed vs. delivered). This ensures alignment before diving into data.
For each hypothesis, list the specific data fields or sources needed—such as ID scan success rates, delivery address restrictions, cancellation reasons, and time-of-day patterns.
Rank the data by availability and potential impact, focusing first on data already logged (e.g., app events, support tickets) before proposing new instrumentation.
Outline how you'd analyze the data (e.g., funnel analysis, cohort comparison, A/B tests) to validate or invalidate each hypothesis, including success metrics.
Suggest actionable next steps based on findings, such as product changes or additional data collection (e.g., logging ID verification failure reasons).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clearly defining the metric and hypotheses, then walk through the assumptions and test statistic calculation, and finally interpret the result in both statistical and practical terms. Emphasize the importance of randomization and checking assumptions before applying the t-test.
Pro tip: Mention that while the t-test is straightforward, in practice you'd also consider sequential testing or variance reduction techniques to increase sensitivity, and always tie the statistical significance back to business impact.
State the null hypothesis (no difference in mean completion rate between control and treatment) and alternative hypothesis (two-sided or one-sided difference). Clearly define the metric: proportion of completed alcohol orders per user or per session.
Verify independence (random assignment), approximate normality (with large sample sizes, CLT applies), and homogeneity of variances (consider Welch's t-test if unequal). Discuss potential issues like ratio metrics or clustered data.
Calculate the t-statistic: difference in sample means divided by pooled standard error. Specify degrees of freedom and whether using Student's or Welch's t-test. Mention that for proportions, the t-test is approximate but valid for large n.
Compare t-statistic to critical value or compute p-value. Report confidence interval for the difference and effect size (e.g., relative lift) to assess practical significance.
Interpret in context: if p < alpha, reject null and conclude evidence of effect; discuss magnitude, business implications, and any caveats (e.g., multiple testing, novelty effects).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.