Citadel·Data Scientist·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Citadel data science interview that was basically a full ML system design session. They wanted you to walk through two complete workflows from raw data to deployed model, regression and classification, and actually justify every decision along the way. Dense and technical, felt more like a take-home prompt than a phone screen.

Questions Asked (1)

Design and implement two end-to-end ML workflows on tabular data: one regression task predicting a continuous target and one classification task predicting a binary or multiclass label. For each, walk through data cleaning, train/val/test splitting, model selection, evaluation, tuning, reproducibility, and interpretability.

Data ModelingTechnical Trade-offsRoot Cause Analysis

Author's notes

This is a beast of a question and I did not pace myself well.

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AI HintsAI Generated

Suggested Approach

Structure your answer by walking through a unified ML pipeline for both tasks, highlighting where regression and classification diverge (e.g., target transformation, loss functions, evaluation metrics). Emphasize rigorous validation, reproducibility, and interpretability, and tie choices to business impact and trade-offs relevant to Citadel's data-driven environment.

Pro tip: Demonstrate awareness of data leakage and temporal dependencies—Citadel often deals with time-series or financial data, so mention using time-based splits and avoiding look-ahead bias. Also, quantify the cost of false positives vs. false negatives to show business acumen.

1. Data Cleaning and Preprocessing

Handle missing values, outliers, and categorical encoding. For regression, consider target transformations (e.g., log) if skewed; for classification, check class balance and consider resampling or class weights.

2. Splitting Strategy and Validation

Use train/validation/test splits (e.g., 60/20/20) with stratification for classification and random or time-based splits for regression. Ensure no leakage and consider cross-validation for robustness.

3. Model Selection and Tuning

Start with baselines (linear regression/logistic regression), then try tree-based models (XGBoost, LightGBM) and neural networks. Use grid/random search or Bayesian optimization for hyperparameter tuning, monitored on validation set.

4. Evaluation and Interpretability

For regression: RMSE, MAE, R²; for classification: accuracy, precision/recall, F1, AUC-ROC, confusion matrix. Use SHAP, permutation importance, or partial dependence plots to explain model decisions.

5. Reproducibility and Deployment

Set random seeds, version data and code, log experiments (e.g., MLflow), and containerize the model. Document preprocessing steps and ensure the pipeline is reproducible end-to-end.

Key Points to Mention

Handling class imbalance in classification (e.g., SMOTE, class weights) and skewed targets in regression (e.g., log transform).
Choice of evaluation metrics aligned with business objectives (e.g., cost-sensitive metrics for classification).
Use of cross-validation and nested CV for reliable hyperparameter tuning.
Feature importance and model-agnostic interpretability methods (SHAP, LIME) for stakeholder trust.
Reproducibility practices: seed setting, environment management, and experiment tracking.
Trade-offs between model complexity, interpretability, and performance (e.g., linear vs. gradient boosting).

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