← Two Sigma Interview Insights
I started okay, talked about framing it as a regression problem and leaned into time-series features pretty early.
Start by clarifying the problem scope and success metrics, then outline a structured approach covering data collection, feature engineering, model selection, and evaluation. Emphasize the time-series nature of the problem and the need to handle temporal dependencies and external factors.
Pro tip: Mention that you would build a baseline model first (e.g., historical average by station and hour) to set a performance benchmark, and then iterate with more complex models. This shows practical wisdom and a focus on incremental value.
Ask clarifying questions about prediction horizon, granularity (station-level, hourly/daily), and what 'available' means (bikes vs. docks). Define evaluation metrics like MAE, RMSE, or MAPE, and consider business impact.
Identify and gather relevant data sources: historical bike availability, station metadata, weather data, event calendars, and temporal features. Explore data for patterns, seasonality, and anomalies.
Create features such as time of day, day of week, holidays, weather conditions (temperature, precipitation), lagged availability, rolling averages, and event indicators. Consider station-specific features like capacity and location.
Choose appropriate models: start with simple baselines (e.g., historical average), then try time-series models (ARIMA, Prophet) and machine learning models (gradient boosting, random forest). Consider deep learning (LSTM) if data is large. Use time-based cross-validation.
Evaluate models using hold-out test sets and appropriate metrics. Analyze errors by station, time, and conditions. Iterate on features and models, and consider deployment and monitoring aspects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.