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Walk me through how you would approach a new predictive modeling problem from business question to production output.

Strong candidates frame it as a problem definition exercise first — clarifying the business objective, success metric, and data availability before touching a model. Look for structured thinking across EDA, feature engineering, model selection, validation, and deployment, not just algorithm name-dropping.

How do you handle class imbalance in a classification problem?

Experienced candidates discuss resampling techniques (SMOTE, undersampling), threshold adjustment, class weighting, and evaluation metrics beyond accuracy — precision/recall, F1, AUC-ROC. Candidates who only mention "get more data" signal limited hands-on experience.

Describe a time your model performed well in validation but failed in production. What happened and how did you fix it?

This question separates Kaggle practitioners from production engineers. Look for awareness of data leakage, train-test distribution shift, feature drift, and pipeline bugs. Strong answers include how they diagnosed the issue and built monitoring to catch it earlier next time.

How do you communicate a complex model's findings to a non-technical executive audience?

Top Data Scientists lead with business impact, use plain-language analogies, and visualize uncertainty without hiding it. Look for specific examples of translating model outputs into recommendations that drove a decision — not just "I made a dashboard."

What techniques do you use for feature selection, and how do you decide which features matter?

Mature answers cover both statistical methods (correlation, mutual information, variance thresholds) and model-based approaches (LASSO, tree-based importance, SHAP values). Look for awareness of the curse of dimensionality and overfitting risk in high-dimensional feature spaces.

How do you validate that your model is fair and not encoding historical bias in its predictions?

Strong candidates reference disaggregated performance evaluation across demographic groups, fairness metrics (demographic parity, equalized odds), and tools like Fairlearn or IBM AI Fairness 360. This matters especially in regulated industries — candidates who haven't thought about it signal risk.