Two Sigma·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Two Sigma data scientist interview with a meaty ML system design question centered on a bike-sharing demand forecasting problem. The question was open-ended enough that I kept second-guessing whether I was going deep enough or just rambling.

Questions Asked (1)

Given historical bike-sharing data with fields like station ID, hourly timestamps, pickups, returns, dock capacity, current availability, weather, holidays, and nearby transit or event signals, design a model to predict the number of bike pickups from a specific dock in the next hour. Cover target definition and leakage prevention, feature engineering, train/val/test strategy for time-series data, choice of evaluation metrics and their trade-offs, and how you'd handle overfitting.

System DesignData ModelingTechnical Trade-offs

Author's notes

This one is deceptively broad.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clearly defining the prediction target and establishing a leakage-free time-based split. Then walk through feature engineering, model choice, evaluation metrics, and overfitting mitigation, emphasizing the temporal nature of the problem and the trade-offs involved.

Pro tip: Explicitly discuss how you would prevent target leakage by ensuring that all features are computed using only data available up to the prediction time, and mention that you would validate the model's stability over time using rolling-origin evaluation.

1. Define Target and Prevent Leakage

Specify the target as the number of pickups at a given station in the next hour. Ensure no future information is used by carefully aligning timestamps and excluding any features that would not be available at prediction time.

2. Engineer Relevant Features

Create features from historical pickups (lags, rolling statistics), temporal patterns (hour, day, holiday), weather conditions, station capacity, current availability, and nearby events or transit. Consider interactions and cyclical encoding.

3. Choose Train/Validation/Test Strategy

Use a time-based split (e.g., train on earlier data, validate on a later period, test on the most recent) to mimic real-world forecasting. Consider rolling-origin cross-validation for robustness.

4. Select Evaluation Metrics and Model

Choose metrics like MAE, RMSE, or Poisson deviance based on the business cost of errors. Select a model suitable for count data (e.g., Poisson regression, gradient boosting) and tune hyperparameters.

5. Address Overfitting

Use regularization, early stopping, and cross-validation. Monitor performance on validation sets and simplify the model if needed. Consider ensembling to improve generalization.

Key Points to Mention

Target definition: pickups in the next hour from a specific dock.
Leakage prevention: only use data available up to the prediction time; avoid using future pickups or returns.
Feature engineering: lag features, rolling means, time of day, weather, holidays, station capacity, nearby events.
Time-series split: train on past, validate on future, test on most recent; rolling-origin cross-validation.
Evaluation metrics: MAE, RMSE, Poisson deviance; trade-offs between interpretability and sensitivity to outliers.
Overfitting mitigation: regularization, early stopping, cross-validation, model simplicity, and monitoring performance over time.

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