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Boston Consulting Group·Data Scientist·Take-home Assignment·Intermediate

Intermediate
Jul 2026

Summary

BCG data scientist round that was basically a full ML pipeline exercise. One meaty question covering everything from train/test splits to gradient boosting to generalization techniques. Felt more like a take-home than a live interview.

Questions Asked (1)

Q1

Given a cleaned retail dataset, split it 80/20 with stratification on the target, build a reproducible preprocessing pipeline (standardize numerics, one-hot encode categoricals), train a gradient-boosted tree model, report AUC on the test set, and describe two techniques you'd use to improve generalization if AUC comes out low.

Data ModelingTechnical Trade-offsAlgorithms & Data Structures
Author's notes

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AI HintsAI Generated

Suggested Approach

Walk through the full pipeline step-by-step, emphasizing reproducibility and stratification. After reporting AUC, discuss two concrete techniques to improve generalization, linking them to the bias-variance tradeoff and the specific context of retail data.

Pro tip: Mention that you would set a random seed and use a pipeline object (e.g., sklearn Pipeline) to prevent data leakage, and that you would compare the test AUC to a baseline (e.g., logistic regression) to contextualize performance.

1. Data splitting with stratification

Split the cleaned dataset into 80% train and 20% test using stratified sampling on the target to preserve class distribution. Set a random seed for reproducibility.

2. Build preprocessing pipeline

Create a reproducible pipeline that standardizes numeric features and one-hot encodes categorical features. Fit the pipeline on the training set only and apply it to the test set to avoid leakage.

3. Train gradient-boosted tree model

Train a gradient-boosted tree model (e.g., XGBoost, LightGBM, or CatBoost) on the preprocessed training data. Use early stopping on a validation set to prevent overfitting.

4. Evaluate and report AUC

Compute the AUC on the test set and report it. Also consider reporting confidence intervals or comparing to a baseline model to assess practical significance.

5. Improve generalization if AUC is low

Describe two techniques: (1) regularization (e.g., increase L1/L2 penalty, reduce tree depth, lower learning rate with more trees) and (2) feature engineering or selection (e.g., add interaction terms, remove noisy features, or use target encoding for high-cardinality categoricals).

Key Points to Mention

  • Stratified splitting to maintain class balance in train and test sets
  • Reproducibility via random seeds and pipeline encapsulation
  • Avoiding data leakage by fitting preprocessing only on training data
  • Gradient boosting hyperparameters (learning rate, tree depth, subsampling)
  • AUC as a threshold-independent metric for binary classification
  • Techniques to improve generalization: regularization, early stopping, feature engineering, cross-validation

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