Hudson·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Hudson gave me a pretty involved technical screen for a Software Engineer role, basically a full end-to-end ML case study on a heart disease dataset. One question but it had like seven parts, so it felt more like a take-home crammed into a live session.

Questions Asked (1)

You're given a tabular dataset for predicting heart disease with a binary target column and features like age, blood pressure, cholesterol, and smoking status. Walk through loading and inspecting the data, cleaning it, doing exploratory analysis with visualizations, engineering features like BMI, training a baseline classifier, evaluating it with appropriate metrics, and summarizing what you'd check before deploying the model.

Product Analytics & MetricsTechnical Trade-offsData Modeling

Author's notes

This was basically the whole interview rolled into one prompt.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a clear, end-to-end data science workflow, emphasizing reproducibility and domain reasoning. Walk through each phase—loading, cleaning, EDA, feature engineering, modeling, evaluation, and deployment checks—while highlighting key decisions and trade-offs relevant to a software engineering role.

Pro tip: Emphasize that you'd automate the pipeline and version data and models, because in a production setting, reproducibility and monitoring are as critical as model accuracy.

1. Load and Inspect

Load the dataset (e.g., with pandas), check shape, dtypes, missing values, and summary statistics. Identify the target distribution and potential data quality issues.

2. Clean and Explore

Handle missing values (impute or drop), treat outliers, and correct inconsistencies. Perform EDA with visualizations (histograms, boxplots, correlation heatmap) to understand feature distributions and relationships with the target.

3. Engineer Features

Create domain-informed features like BMI from height and weight, and consider interactions (e.g., age × blood pressure). Encode categorical variables and scale numerical features as needed.

4. Train and Evaluate Baseline

Split data into train/validation/test sets, train a simple classifier (e.g., logistic regression), and evaluate using metrics like accuracy, precision, recall, F1, and AUC-ROC, considering class imbalance.

5. Pre-Deployment Checks

Before deployment, validate model performance on a holdout set, check for fairness across subgroups, ensure interpretability, and plan for monitoring data drift and model retraining.

Key Points to Mention

Data leakage prevention (e.g., fit scalers only on training data)
Class imbalance handling (e.g., using class weights, SMOTE, or appropriate metrics)
Feature importance and model interpretability (e.g., SHAP, coefficients)
Cross-validation for robust performance estimation
Deployment considerations: model versioning, API integration, latency, and monitoring
Ethical and regulatory aspects (e.g., patient privacy, bias in healthcare data)

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