##### Scenario DoorDash is considering a 'Biker Dasher' program to let couriers use bicycles (and e-bikes) for deliveries in dense urban areas. You are the data scientist supporting the launch. ##### Question Work through the end-to-end design, launch, measurement, and post-launch diagnosis of this program. 1. What is the primary business objective of launching the Biker Dasher program? 2. What factors should you evaluate before launching (market/geo fit, supply-demand balance, operational readiness, economics, safety/compliance)? 3. How would you operationalize this program and make it functional day-to-day and at scale (eligibility, onboarding, routing/ETA, dispatch, pricing, merchant ops, support)? 4. What specific data would you collect to analyze parking or curb-space situations for biker dashers? 5. What metrics would you track to measure the program's success? Specify core KPIs, secondary metrics, and guardrails. 6. How would you design the experiment to test the feature, given the two-sided marketplace and network interference? 7. How would you decide the geographic selection and phased rollout strategy? 8. If the metrics do not improve, how would you diagnose the outcome and iterate? ##### Hints Define objectives and pre-launch factors; consider supply, demand, and city constraints; design KPIs with guardrails; use geo/switchback experiments to handle marketplace interference; plan a metric-gated phased rollout; and have a structured root-cause path for a null result.