I jumped straight to restaurant-level breakdowns and forgot to first sanity-check whether the spike was even real, like ruling out a logging bug or a change in how wait time is measured.
Start by validating the metric and checking for data pipeline issues, then segment the spike by time, geography, restaurant, and driver cohorts to isolate the root cause. If understaffing is confirmed, propose a data-driven solution such as dynamic incentives or staffing recommendations, and design an experiment to measure impact.
Pro tip: Frame your answer around the scientific method: hypothesis, test, and iterate. Mention that correlation doesn't imply causation and that you'd rule out alternative explanations like weather or app changes before concluding understaffing.
Check if the spike is real by verifying data quality, pipeline integrity, and metric definitions. Ensure no logging errors or changes in data collection occurred.
Break down wait times by dimensions like time of day, region, restaurant, and driver tenure to identify patterns. Look for correlations with other metrics like order volume or restaurant prep times.
List potential causes (e.g., understaffing, increased demand, app issues) and use data to test each. For understaffing, check if restaurants with higher wait times have lower staff levels or longer prep times.
If understaffing is likely, quantify its effect by comparing wait times at understaffed vs. adequately staffed restaurants. Use statistical methods to estimate the contribution.
Suggest interventions like dynamic driver incentives, restaurant staffing recommendations, or order throttling. Design an A/B test to measure the impact of the chosen solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.