Amazon currently tracks only the number of packages delivered and total delivery time for each driver. Design a robust framework to evaluate delivery-driver performance.

Identify additional data you would collect (e.g., route characteristics, weather, traffic, package weight, customer feedback, vehicle type, stop density, promised delivery windows).

Explain why each data point matters and how you would gather it.

Propose quantitative metrics or a scoring model that fairly compares drivers who operate under different conditions.

Outline how you would surface insights to drivers and managers and iterate on the system over time.

Think about normalizing for factors outside the driver’s control—distance, urban vs. rural routes, real-time weather and traffic, peak season spikes.

Consider leading (process) and lagging (outcome) indicators: safety incidents, on-time rate, customer satisfaction, fuel efficiency.

Discuss statistical or ML techniques (e.g., regression, clustering) to isolate driver impact from external noise.

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