Jane Street·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Jane Street ML Engineer interview, one big technical question that basically covered everything you'd ever want to know about time series forecasting. It was a lot to hold in your head at once.

Questions Asked (1)

You're given a time-series dataset with timestamps and numeric targets. Walk through your full pipeline: preprocessing, modeling choices, evaluation strategy, and how you'd handle concept drift over time.

System DesignTechnical Trade-offsData Modeling

Author's notes

This question is basically five questions glued together and they let you run with it.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a clear pipeline: start with data validation and exploratory analysis, then discuss feature engineering and model selection with a focus on time-series cross-validation, and finally address evaluation metrics and drift handling. Emphasize practical trade-offs and how you would monitor and adapt the model in production.

Pro tip: At Jane Street, they care about robustness and adaptability. Show you understand that financial time series are non-stationary and that evaluation must mimic real-world deployment, e.g., using walk-forward validation and considering transaction costs if applicable.

1. Data Validation and Exploration

Check for missing values, outliers, and irregularities in timestamps. Plot the series to identify trends, seasonality, and structural breaks.

2. Feature Engineering and Preprocessing

Create time-based features (lags, rolling statistics, calendar effects) and handle stationarity via differencing or detrending. Scale features if needed, but be cautious with look-ahead bias.

3. Model Selection and Validation

Choose models suitable for time series (e.g., ARIMA, gradient boosting, LSTM) and use time-series cross-validation (e.g., expanding window) to avoid data leakage. Consider ensembling for robustness.

4. Evaluation Strategy

Use metrics like MAE, RMSE, or MAPE, but also evaluate directional accuracy if relevant. Backtest on a hold-out period that simulates real-time forecasting.

5. Concept Drift Handling

Monitor performance over time and detect drift via statistical tests (e.g., ADWIN, Page-Hinkley). Retrain periodically or use online learning to adapt to changes.

Key Points to Mention

Time-series cross-validation (e.g., walk-forward validation) to prevent look-ahead bias
Feature engineering with lagged variables, rolling windows, and domain-specific indicators
Model choices: statistical (ARIMA, exponential smoothing) vs. machine learning (XGBoost, LSTM) and their trade-offs
Evaluation metrics that account for time dependence and possibly asymmetric costs
Concept drift detection methods and adaptation strategies (retraining, online learning, ensemble weighting)
Production considerations: monitoring, retraining frequency, and pipeline automation

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