Data Science Interview Questions and Answers (2025): The Ultimate Guide

The data science interview is a multi-stage gauntlet designed to test your technical depth, business acumen, and communication skills under pressure. Passing it requires more than just knowledge; it requires strategic preparation.

This guide, written by data science hiring managers, is your strategic prep kit. We've compiled the most frequently asked questions across every core data science competency, updated for 2025. Use this as your roadmap. Master these concepts, and you'll walk into your interviews with confidence.

How to Use This Guide for Maximum Impact

Don't just read the questions. For each one, practice articulating the answer out loud. Whiteboard the solution. Write the code. This guide provides a handful of sample answers to show you how to structure your response. The full, detailed answers to every question are in our comprehensive eBook.

SQL Interview Questions

SQL is non-negotiable. You will have a technical screen focused on SQL, and you will be expected to write queries on a whiteboard or in a shared editor. They are testing your ability to manipulate data efficiently.

Sample Question & Answer:

Q: What is the difference between LEAD() and LAG()? When would you use them?

A: Both are window functions used to access data from a different row in the same result set without a self-join. LAG() accesses data from a previous row, while LEAD() accesses data from a subsequent row. A classic use case is calculating the time difference between consecutive events, like user logins or purchases. You'd use LAG(timestamp) to get the previous event's time to subtract it from the current row's timestamp.

More Essential SQL Questions:

1. What’s the difference between ROW_NUMBER(), RANK(), and DENSE_RANK()?
2. Explain the difference between a LEFT JOIN and an INNER JOIN. What happens to rows from the left table that have no match in the right table in each case?
3. How would you find the second-highest salary from an employees table?
4. What is a Common Table Expression (CTE) and why is it useful?
5. You have a table of user transactions. Write a query to get the total revenue per user for users who made their first purchase in the last 30 days.

Python & Pandas Interview Questions

You'll face questions that test your mastery of Python's data stack, especially Pandas and NumPy. The focus is on practical data manipulation, cleaning, and preparation for modeling.

Sample Question & Answer:

Q: What is the difference between .loc, .iloc, and .ix in Pandas?

A: .loc is label-based, meaning you select data based on the index labels and column names. .iloc is integer-position-based, so you use integer indices like in a standard Python list. .ix was a confusing hybrid of both and is now deprecated; you should always use .loc for labels and .iloc for integer positions to write clear, predictable code.

More Essential Python Questions:

1. How do you handle missing values (NaNs) in a Pandas DataFrame? Describe three different methods.
2. What is the difference between apply() and map() on a Pandas Series?
3. Explain list comprehensions and why they are considered more "Pythonic" than traditional for-loops for creating lists.
4. You have a DataFrame with a user_id and purchase_date. How would you find each user's most recent purchase date using groupby()?

Statistics & Probability Interview Questions

This is where interviewers separate candidates who just know how to call .fit() from true data scientists. You must understand the "why" behind the models.

Sample Question & Answer:

Q: What is a p-value? What does a p-value of 0.03 mean?

A: The p-value is the probability of observing a result as extreme or more extreme than the one you measured, assuming the null hypothesis is true. A p-value of 0.03 means that if there were truly no effect (the null hypothesis), there would only be a 3% chance of seeing the data you saw, or something more extreme, just by random luck. If our significance level (alpha) is 0.05, we would reject the null hypothesis and conclude the result is statistically significant.

More Essential Statistics Questions:

1. Explain the difference between Type I and Type II errors. Which one is worse?
2. What is the Central Limit Theorem and why is it important for data scientists?
3. Describe the bias-variance tradeoff. Give an example of a model with high bias and one with high variance.
4. What is statistical power? How can you increase it?

Machine Learning Concepts Interview Questions

These questions test your conceptual understanding of algorithms, their assumptions, and their tradeoffs.

1. Explain how a Random Forest model works. Why is it less prone to overfitting than a single Decision Tree?
2. What is regularization (e.g., L1 and L2)? Why is it used?
3. How would you handle an imbalanced dataset, for example, in fraud detection where only 1% of transactions are fraudulent?
4. What are precision and recall? When would you optimize for one over the other?
5. Explain gradient boosting machines (like XGBoost or LightGBM) to a non-technical stakeholder.

A/B Testing & Experimentation Interview Questions

For product-focused roles, this is critical. Companies live and die by their ability to experiment correctly.

1. What is Sample Ratio Mismatch (SRM)? Why is it a serious problem?
2. How do you determine the required sample size for an A/B test? What inputs do you need?
3. What are some common pitfalls of A/B testing? (e.g., peeking, multiple comparisons)
4. Explain CUPED (Controlled-experiment using Pre-Experiment Data). How does it help?

ML System Design Interview Questions

For mid-level and senior roles, you'll be asked to design an end-to-end ML system. This tests your architectural and practical skills.

1. Design a recommendation system for an e-commerce site like Amazon.
2. How would you build a system to detect spam comments on a social media platform?
3. Design an ETA (Estimated Time of Arrival) prediction model for a ride-sharing app.
4. How would you monitor a deployed model for performance degradation or concept drift?

Don't Get Stuck: Learn our step-by-step framework for answering any system design question.
Read it here: Machine Learning System Design Interview: A Step-by-Step Framework

Product & Business Sense Interview Questions

Data science doesn't exist in a vacuum. You must demonstrate that you can connect your technical work to business impact.

1. Our user engagement is down 10% week-over-week. How would you investigate?
2. What would be the north-star metric for a platform like YouTube? What are some good counter-metrics?
3. Should we launch a new feature that increases revenue by 5% but decreases user retention by 2%?
4. "Tell me about a project you worked on." (This is the most common question of all!)

NEW for 2025: GenAI & LLM Interview Questions

The landscape has changed. Awareness of Large Language Models (LLMs) is now essential for many roles.

1. What is RAG (Retrieval-Augmented Generation) and why is it a popular pattern for building chatbots?
2. How would you evaluate the quality of an LLM's responses for a customer support bot?
3. What are embeddings? How are they used in semantic search?
4. What are the key challenges in deploying LLMs to production (e.g., cost, latency, safety)?

Preparing for Different Seniority Levels

• Entry-Level: Focus on rock-solid fundamentals. Be an expert in SQL, Python, and core ML/Stat concepts. Be ready to explain your university or personal projects in great detail.
• Mid-Level: All of the above, plus proven project impact. Expect questions about ML system design, A/B testing, and influencing product decisions.
• Senior/Lead: Expect a heavy focus on system design, strategy, and leadership. Questions will be open-ended: "How would you build the data science function for this team?" "Tell me about a time you mentored a junior data scientist."

Ready to Land the Offer? Go from Prepared to Unstoppable.

Knowing the questions is half the battle. The other half is delivering clear, confident, and structured answers under pressure. That's where we come in.

• Book your free 10min call with us: https://datadrivenprep.com/products/1-on-1-free-consultation-before-mock-interview