When building predictive models, you often notice performance differences between training and validation data.

You need to explain why this happens and how to manage it.

Describe that bias is error from erroneous assumptions (under‑fitting) and variance is error from sensitivity to small fluctuations in the training set (over‑fitting). Explain that the tradeoff involves balancing model complexity to minimize total error, using techniques like cross‑validation, regularization, or ensemble methods.

A clear explanation shows you understand model generalization and can choose appropriate strategies to improve performance.

At my previous role, the marketing team needed to predict customer churn to target retention campaigns.

Design and deliver an end‑to‑end churn prediction pipeline.

Collected raw logs from the data lake, performed ETL with Spark, engineered features (recency, frequency, monetary), split data, trained several models (logistic regression, XGBoost), selected the best via AUC, containerized the model with Docker, and deployed to a REST API using Kubernetes. Created dashboards in Tableau for stakeholders and documented model assumptions.

The model achieved a 12% lift in retention rates, saving $250K annually, and was adopted as a core component of the CRM workflow.

While preparing a sales forecasting dataset, 8% of records had missing values in the 'discount' column.

Decide on an appropriate imputation strategy.

Analyzed missingness pattern, determined it was Missing At Random. Compared simple mean imputation, median imputation, and model‑based imputation (KNN). Chose median imputation for its robustness to outliers and documented the approach.

The cleaned dataset improved model RMSE by 4% compared to using mean imputation, and the process was reproducible for future data loads.

During model tuning for a linear regression on housing prices, overfitting was observed.

Introduce regularization to improve generalization.

Implemented L1 (Lasso) which adds the absolute value of coefficients to the loss, encouraging sparsity and feature selection. Also tried L2 (Ridge) which adds squared coefficients, shrinking them uniformly without eliminating features. Compared performance via cross‑validation.

L1 reduced the feature set by 30% with negligible loss in accuracy, while L2 provided a smoother coefficient shrinkage and slightly better validation RMSE. Chose L1 for interpretability.

The product team relied on intuition for feature prioritization, leading to missed market opportunities.

Demonstrate the value of a data‑driven roadmap.

Built a simple predictive model showing potential revenue uplift for top‑ranked features, created visual dashboards, and presented a cost‑benefit analysis in a stakeholder workshop. Addressed concerns by outlining data sources, model assumptions, and a pilot plan.

Stakeholders approved a pilot, resulting in a 15% increase in feature adoption and a $500K revenue boost in the first quarter after rollout.

A streaming service wants to test a new recommendation engine against the current one.

Design a statistically sound experiment to measure lift in user engagement.

Define primary metric (e.g., average watch time), secondary metrics (click‑through rate, churn). Randomly assign users to control and treatment groups ensuring equal exposure. Determine sample size using power analysis (80% power, 5% significance). Run test for a sufficient period to capture variability, monitor for anomalies, and use t‑test or Bayesian analysis to compare groups. Plan for post‑test analysis to assess segment‑level effects.

The test showed a 6% increase in average watch time with statistical significance (p=0.02). The algorithm was rolled out to 30% of users, leading to a projected $1.2M quarterly revenue increase.