DRW·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

DRW ML Engineer interview that was essentially a single applied coding problem. The whole thing centered on building a sklearn pipeline for imbalanced classification, which sounds manageable until you're actually in it trying to remember whether SMOTE goes before or after the scaler.

Questions Asked (1)

Using scikit-learn and imbalanced-learn, build an end-to-end classification pipeline that handles severe class imbalance. It should include standard preprocessing, a resampling step, a classifier, and report precision, recall, and F1 on a held-out test set.

Technical Trade-offsAlgorithms & Data StructuresSystem Design

Author's notes

The resampling placement tripped me up more than I expected.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a clear, modular pipeline: start with a train-test split, then build a ColumnTransformer for preprocessing, apply a resampling technique (e.g., SMOTE) only to the training data, and train a classifier. Evaluate on the untouched test set using precision, recall, and F1, and discuss trade-offs and alternatives.

Pro tip: Always apply resampling inside a pipeline or only to the training fold to avoid data leakage; use imbalanced-learn's Pipeline to combine resampling and classification seamlessly.

1. Data splitting and preprocessing

Split the data into training and test sets with stratification to preserve class ratios. Build a preprocessing pipeline using ColumnTransformer to handle numerical and categorical features (e.g., scaling, one-hot encoding).

2. Resampling strategy

Choose a resampling method like SMOTE, ADASYN, or RandomUnderSampler based on the imbalance severity and dataset size. Apply it only to the training data to prevent leakage.

3. Pipeline construction

Use imbalanced-learn's Pipeline to chain preprocessing, resampling, and a classifier (e.g., RandomForest, LogisticRegression). This ensures resampling is applied only during training.

4. Model training and evaluation

Fit the pipeline on the training set and predict on the held-out test set. Compute precision, recall, and F1 score using scikit-learn's metrics, focusing on the minority class.

5. Trade-offs and improvements

Discuss the impact of resampling on precision-recall balance, consider alternative approaches like class weights or threshold tuning, and mention cross-validation with resampling inside each fold.

Key Points to Mention

Use of imbalanced-learn's Pipeline to prevent data leakage by applying resampling only to training folds.
Choice of resampling technique (SMOTE, ADASYN, undersampling) and its impact on precision vs. recall.
Stratified splitting to maintain class distribution in train and test sets.
Evaluation metrics: precision, recall, F1, and possibly AUC-PR for imbalanced data.
Alternative strategies: class weights, threshold moving, or ensemble methods like BalancedRandomForest.
Cross-validation with resampling inside each fold to get robust performance estimates.

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