More open-ended than I expected for an OA.
Start by clarifying the problem scope and data assumptions, then walk through the ML pipeline from data collection to deployment, emphasizing trade-offs at each stage. Focus on demonstrating structured thinking and practical considerations for a production system.
Pro tip: Explicitly discuss how you would handle class imbalance and potential data leakage, as these are common pitfalls in financial classification problems. Also, mention the importance of model interpretability for regulatory compliance in fintech.
Define the target variable (can buy a house) and discuss data sources, such as financial records, credit scores, and demographic data. State assumptions about data availability, quality, and potential biases.
Outline preprocessing steps like handling missing values, encoding categorical variables, and scaling numerical features. Design features such as debt-to-income ratio, savings, and credit history that are predictive of home-buying ability.
Choose appropriate models (e.g., logistic regression, gradient boosting) considering interpretability and performance. Discuss hyperparameter tuning and handling class imbalance with techniques like SMOTE or class weights.
Use cross-validation and a holdout set to assess model performance. Select evaluation metrics like AUC-ROC, precision-recall, and F1-score, and discuss business implications of false positives vs. false negatives.
Discuss deployment considerations, such as model serving, latency, and monitoring for data drift. Emphasize the need for periodic retraining and interpretability for stakeholder trust.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The time-aware validation piece is what separates this from a generic regression question.
Structure your answer as a clear, end-to-end pipeline that emphasizes time-series awareness and leakage prevention. Start with data validation and exploratory analysis, then move through feature engineering, time-aware validation, model selection and tuning, and finally submission generation. Highlight trade-offs and justify choices with practical reasoning.
Pro tip: Explicitly call out how you prevent data leakage at every stage—especially in feature engineering and cross-validation—since this is a common pitfall in time-series problems and a key differentiator at top firms like Citadel.
Load train/test CSVs, check for missing values, outliers, and temporal consistency (e.g., timestamps, gaps). Plot power output and weather variables over time to understand patterns and seasonality.
Create features like lags, rolling statistics, time-of-day, and weather interactions, ensuring all transformations use only past data. Avoid using future information (e.g., target encoding without proper time splits).
Use a rolling or expanding window cross-validation scheme (e.g., TimeSeriesSplit) to mimic real-world forecasting. Never shuffle data; maintain chronological order to prevent leakage.
Start with a simple baseline (e.g., linear regression) and progress to tree-based models (XGBoost, LightGBM) or neural networks. Tune hyperparameters using the time-aware validation, and consider ensembling.
Retrain the best model on the full training set (or use the best iteration from CV), predict on the test set, and format the output as required (e.g., CSV with timestamps and predictions). Validate submission format and sanity-check predictions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.