Voleon Group·Data Scientist·Take-home Assignment·Senior

Senior

May 2026

Summary

Take-home assignment for a Data Scientist role at Voleon Group, heavy on end-to-end ML pipeline work with a lot of moving parts crammed into one task. Felt more like a mini-project than a question.

Questions Asked (1)

Build a full scikit-learn pipeline to predict signups from ad spend, clicks, CPC, region, and a time trend derived from date. The pipeline should handle preprocessing (scaling numeric features, one-hot encoding region), optionally apply a log transform to the target based on a diagnostic check, add polynomial features, and tune Ridge, Lasso, and ElasticNet via GridSearchCV with nested cross-validation. Report RMSE and R-squared on a held-out test set, extract and rank the top 10 features by absolute standardized effect, save the model, and show reload/scoring code.

Technical Trade-offsData ModelingAlgorithms & Data Structures

Author's notes

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Suggested Approach

Structure your answer around a modular scikit-learn Pipeline that integrates preprocessing, optional target transformation, feature engineering, and model tuning. Emphasize the use of nested cross-validation for unbiased performance estimation and proper handling of data leakage. Conclude with model interpretation, saving, and reloading to demonstrate production readiness.

Pro tip: Always perform the log-transform diagnostic (e.g., check skewness or residual normality) inside the pipeline using a custom transformer to avoid data leakage. Use TransformedTargetRegressor to seamlessly apply the log transform and inverse it for predictions.

1. Data Preparation and Diagnostic

Load and split data into train/test. Check target distribution (e.g., skewness) to decide whether to apply log transform. Create a time trend feature from date (e.g., days since start).

2. Build Preprocessing Pipeline

Create a ColumnTransformer to scale numeric features (ad spend, clicks, CPC, time trend) and one-hot encode region. Optionally include polynomial features (degree 2) for numeric features.

3. Integrate Target Transformation and Model

Use TransformedTargetRegressor with a log1p transform if diagnostic indicates skew. Chain preprocessing and regressor (Ridge, Lasso, ElasticNet) into a full Pipeline.

4. Hyperparameter Tuning with Nested CV

Set up GridSearchCV with inner CV for hyperparameter search and outer CV for performance estimation. Define parameter grids for each model (e.g., alpha for Ridge/Lasso, alpha and l1_ratio for ElasticNet).

5. Evaluation, Interpretation, and Deployment

Evaluate best model on held-out test set using RMSE and R-squared. Extract coefficients, standardize them (if needed), and rank top 10 features by absolute effect. Save model with joblib and demonstrate reloading and scoring.

Key Points to Mention

Use of ColumnTransformer for mixed data types and to prevent data leakage.
Nested cross-validation to avoid overfitting during hyperparameter tuning and provide unbiased performance estimate.
TransformedTargetRegressor for log transform of target, ensuring inverse transform for predictions.
PolynomialFeatures for capturing non-linear relationships, but be mindful of dimensionality and potential overfitting.
Standardization of features before regularization and for comparing feature effects.
Model persistence with joblib and reloading for scoring new data.

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