Delivery Driver Performance Evaluation Framework
Company: Amazon
Role: Product Manager
Category: Product / Decision Making
Difficulty: hard
Interview Round: Technical Screen
##### Question
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.
##### Hints
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.
Quick Answer: Practice designing a fair delivery-driver performance framework that goes beyond package count and total time. The solution covers route difficulty, weather, traffic, package attributes, vehicle data, safety gates, expected-versus-actual scoring, coaching dashboards, bias monitoring, and metric governance.