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.
Quick Answer: 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.