Improve biker delivery with metrics and levers

Case: Optimize delivery performance for bike couriers

You are a Data Scientist at a food-delivery marketplace (e.g., DoorDash/Uber Eats). Your team focuses on bike couriers in dense cities.

Leadership asks: “What should we optimize for, and how would you do it?”

Context

Bike delivery performance is affected by many external factors:

• Geography & built environment: hills, bridges, plazas vs high-rise malls, downtown vs suburbs, parking/locking availability.
• Weather & traffic conditions.
• Merchant attributes: cuisine type (hot/cold, wet/dry), packaging, order prep speed/variance, how orders are handed off to couriers.
• Customer demand: urgency, distance to merchant, order size/quality sensitivity.
• Marketplace interactions: merchant–courier and courier–customer supply/demand matching .

Task

1. Propose a clear objective for “better” biker delivery (what are you trying to improve?).
2. Define a metrics framework including:
   • Primary (north star) metric(s)
   • Diagnostic metrics (to explain movement)
   • Guardrail metrics (to prevent harm) Consider perspectives of couriers, customers, and merchants .
3. List key data/features you would need (and any data quality risks).
4. Suggest actionable levers/ideas (product, ops, routing/matching, merchant experience) you would test or roll out.
5. Outline an experimentation/causal plan to validate impact given strong confounding from weather, geography, time of day, and demand fluctuations.

Output

Provide the metric definitions (with formulas where helpful), and a concrete plan of analysis/experiments you would run to decide what to ship.