25 Data Scientist Interview Questions & Answers for 2026

25 Data Scientist Interview Questions & Answers for 2026

Data science interviews combine statistics, machine learning, SQL/Python coding, and business sense. Here are the most-asked questions with AI-enhanced preparation strategies.

Statistics & ML Theory (10)

1. "Explain the bias-variance tradeoff" — Underfitting vs overfitting
2. "What's the difference between L1 and L2 regularization?"
3. "How would you handle imbalanced datasets?"
4. "Explain cross-validation and when to use it"
5. "What's the difference between bagging and boosting?"
6. "Describe the assumption of linear regression"
7. "How do you evaluate a classification model?"
8. "Explain PCA and when to use it"
9. "What's the difference between parametric and non-parametric models?"
10. "How do you handle multicollinearity?"

SQL & Coding (8)

1. "Write a query to find the second highest salary" — Window functions
2. "Find duplicate records in a table" — GROUP BY + HAVING
3. "Calculate running average of daily revenue" — Window functions
4. "Implement k-means clustering from scratch" — Python
5. "Write a function for feature engineering from timestamps"
6. "Parse and clean messy JSON data" — Pandas
7. "Implement logistic regression gradient descent from scratch"
8. "Optimize a slow query on a 100M row table" — Indexing

Business Case (7)

1. "How would you A/B test a new recommendation algorithm?"
2. "Our click-through rate dropped 15% — investigate"
3. "How would you build a churn prediction model?"
4. "Design a fraud detection system"
5. "Estimate the value of a new feature using causal inference"
6. "How would you measure the impact of a marketing campaign?"
7. "Explain your ML model to a non-technical executive"

Practice with AI

Use AissenceAI's mock interview feature for data science-specific questions. The AI copilot helps with SQL syntax, statistical explanations, and business case frameworks in real-time.