Ride-Hailing Supply Estimate: Active Uber Drivers Serving a Mid-Sized American City
Company: Uber
Role: Product Manager
Category: Product / Decision Making
Difficulty: medium
Interview Round: Other
You are asked to estimate how many drivers Uber has in a mid-sized city in the United States. Lay out the structure of your estimate, the assumption behind every input and how you would check that the final number makes sense.
```hint Decide what you are counting
Drivers online at one moment and drivers who work at some point during a month are very different numbers. Pick one, and think about what links the two.
```
### Clarifying Questions
- Does "mid-sized city" mean the city limits or the wider metro area, and is there a population you want me to anchor on?
- Should the count be everyone who completes at least one trip in a month, or the number of drivers online during the busiest hour?
- Is the scope rides only, or should people who only deliver food and packages through the platform count too?
- If a driver also works for Lyft, should they count as one of Uber's drivers?
### What a Strong Answer Covers
- A precise definition of the number being estimated and a stated population anchor for the city.
- An equation that turns rider demand into the driver time it consumes, with every input labeled as an assumption and given a round value.
- Realistic driver time: trip length, time spent heading to pickups or waiting between requests, and the part-time hours most drivers work.
- Correct arithmetic, at least one independent sanity check, and a range showing which inputs move the answer most.
### Follow-up Questions
- Which input is your estimate most sensitive to, and what data inside Uber would you use to replace it?
- How would you use the same model to decide how many additional drivers to attract for a Friday night?
- If the interviewer meant the whole metro area rather than the city, which inputs besides population would change?
Overview: An estimation question for product manager candidates that asks how many people drive for Uber in a typical mid-sized US city. It tests defining the number to estimate, turning rider demand into driver hours with labeled assumptions, accounting for part-time drivers and idle time, and sanity-checking the result with a range.