Predict bike demand and avoid overfitting
Company: Two Sigma
Role: Data Scientist
Category: Machine Learning
Difficulty: hard
Interview Round: Technical Screen
You are given historical data for a city bike-sharing system. Available fields include `station_id`, hourly timestamp, number of bike pickups and returns, dock capacity, current bikes available at prediction time, weather, holidays, and nearby transit or event signals.
Design a model to predict the number of bike pickups from a specific dock during the next hour.
Discuss:
- how you would define the target and avoid data leakage;
- what features you would engineer from temporal patterns, station behavior, weather, and geography;
- what train/validation/test strategy you would use for this time-dependent problem;
- which evaluation metric(s) you would choose (for example, MAE, RMSE, Poisson deviance, or a downstream empty/full-dock metric) and the trade-offs;
- how you would detect and prevent overfitting.
Overview: This question evaluates time-series forecasting, feature engineering, awareness of data leakage risks, model evaluation choices, and overfitting prevention competencies within the Machine Learning domain applied to demand prediction.
Community answers
Answer by miriam.bundy06
Interviewer: Let's do a case study. How would you predict the number of available bikes at a Citi Bike station?
Candidate: Before I dive in, a few clarifying questions. What data do we have access to, is it trip-level records, or hourly snapshots of bike counts per station, or both? And what's the time horizon, how far back does the data go?
Interviewer: You have both. Trip-level records with start time, end time, start station, end station, going back two years. And hourly snapshots of bike count and empty docks at each station over the same period.
Candidate: Great, that's helpful. Two more things. Is this analysis meant to run once, or eventually be deployed continuously, say to support a rebalancing dashboard for ops trucks? And are we predicting for one station or citywide, across all stations?
Interviewer: Assume citywide, and assume it eventually runs in production to support rebalancing decisions.
Candidate: Got it, that changes my approach in two ways. First, since it's citywide and needs to generalize to new stations that might get added later, I'd want my target variable to be something that transfers across stations rather than raw bike count. Second, since it's eventually production, I'd want to think about retraining cadence and failure modes, not just a one-off model fit.
Interviewer: Good, go ahead and lay out your approach.
Candidate: I'd start simple and build up. As a baseline, I'd bucket by hour of week, twenty-four hours times seven days, and take the his