Voleon Group·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Voleon DS tech round, one question, very applied. They gave you a small financial time-series table and asked you to write the full preprocessing pipeline in pandas before fitting a linear regression. Not conceptual at all, just get it done.

Questions Asked (1)

Given a financial time-series dataset with date, two feature columns, and a target column, write Python/Pandas code to drop nulls, winsorize extreme values at the 1st and 99th percentiles, standardize the predictor columns, and produce an X and y pair ready for linear regression.

Data ModelingTechnical Trade-offsAlgorithms & Data Structures

Author's notes

The winsorizing part is where I fumbled a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data structure and requirements, then outline a clean, reproducible pipeline using Pandas and Scikit-learn. Emphasize avoiding data leakage by fitting transformations only on training data, and discuss trade-offs of winsorization and standardization.

Pro tip: Always split into train/test before fitting any transformations to prevent data leakage; use pipelines to encapsulate preprocessing steps for production readiness.

1. Clarify and Inspect Data

Confirm column names, data types, and time-series ordering. Check for missing values and understand the distribution of features and target.

2. Handle Missing Values

Drop rows with nulls in relevant columns using dropna(), but consider if imputation is more appropriate for time-series data.

3. Winsorize Extreme Values

Compute 1st and 99th percentiles for each predictor column and clip values to these bounds to reduce outlier impact.

4. Standardize Predictors

Scale predictor columns to zero mean and unit variance using StandardScaler, fitting only on training data to avoid leakage.

5. Produce X and y

Separate the target column as y and the standardized predictors as X, ensuring they are aligned and ready for linear regression.

Key Points to Mention

Data leakage prevention: fit transformations on training set only
Winsorization vs. trimming: clipping preserves data points but reduces influence
Standardization importance for linear regression with regularization
Time-series considerations: avoid shuffling, use chronological splits
Use of Pipeline to encapsulate preprocessing and model
Handling missing values: drop vs. impute based on missingness mechanism

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