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Google·Product Manager·Onsite - Product Sense / Strategy·Intermediate

Intermediate
Jun 2026

Summary

Google PM interview with a classic estimation question about bike-sharing. Pretty open-ended, which sounds easy but the lack of constraints is where you can lose the thread fast.

Questions Asked (1)

Q1

You're launching a bike-sharing service in a city of your choice. How many bikes would you need?

Product Sense & IdeationProduct Analytics & MetricsProduct Strategy
Author's notes

Picked a mid-sized city thinking it'd be easier to reason about than NYC or something massive.

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AI HintsAI Generated

Suggested Approach

Pick a specific city and state your assumptions about its population, density, and cycling culture. Then estimate demand by segmenting potential users and calculating required bikes based on usage patterns and operational constraints like rebalancing and maintenance.

Pro tip: Show that you understand the difference between fleet size and daily active bikes, and mention that you'd validate your estimate with a pilot before full launch.

1. Choose a city and state assumptions

Select a city (e.g., San Francisco) and clearly state key assumptions: population, area, bike-friendliness, tourism, and existing alternatives.

2. Segment the market and estimate demand

Break potential users into segments (commuters, students, tourists) and estimate adoption rates and trips per user per day to get total daily trips.

3. Convert demand to bike supply

Use average trips per bike per day (considering trip duration, turnaround, and peak hours) to calculate the number of bikes needed to meet daily demand.

4. Adjust for operational factors

Add buffers for maintenance, rebalancing, and peak demand to determine total fleet size.

5. Sanity-check and iterate

Compare your estimate to real-world benchmarks (e.g., NYC Citi Bike) and suggest a pilot to refine the number.

Key Points to Mention

  • Population and density of the chosen city
  • Market segmentation (commuters, tourists, etc.) and adoption rates
  • Average trips per bike per day and peak demand
  • Operational factors: maintenance, rebalancing, and bike availability
  • Benchmarking against existing bike-sharing systems
  • Proposing a pilot to validate assumptions

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.