---
title: "Data Scientist Interview Questions: 45+ Questions with Answers (2026)"
description: "Comprehensive list of data scientist interview questions covering statistics, machine learning, SQL, Python, and case studies. Asked at Google, Meta, Netflix, and top tech companies."
canonical: "https://mortit.com/blog/data-scientist-interview-questions"
---
Interview Prep

# Data Scientist Interview Questions

45+ questions asked at Google, Meta, Netflix, and top tech companies-with explanations for each answer.

18 min read Updated February 2026

TL;DR

Data science interviews test five areas: **Statistics** (hypothesis testing, probability),**Machine Learning** (algorithms, evaluation), **SQL** (joins, window functions),**Coding** (Python/pandas), and **Business Cases** (metrics, experimentation). The best candidates explain the "why" behind their answers-not just the "what."

## The Five Types of Data Science Questions

Data science interviews are uniquely broad. You need to demonstrate both technical depth and business thinking.

| Type | What It Tests | Example |
| --- | --- | --- |
| **Statistics** | Probability, hypothesis testing, experimental design | "How would you design an A/B test?" |
| **Machine Learning** | Algorithms, model selection, evaluation | "Explain the bias-variance tradeoff" |
| **SQL** | Data manipulation, query writing | "Find the second-highest salary" |
| **Coding** | Python, pandas, data wrangling | "Clean and transform this dataset" |
| **Case Study** | Business thinking, metrics, communication | "How would you measure the success of Reels?" |

## Statistics & Probability Questions

These questions test your foundational understanding. Many candidates memorize formulas but can't explain the intuition.

### Probability

1.  **What's the probability of getting at least one 6 when rolling a die 4 times?**
2.  **You have two coins: one fair, one always heads. You pick one randomly and flip it 3 times, getting 3 heads. What's the probability it's the fair coin?**
3.  **Explain Bayes' theorem. Give a real-world example.**
4.  **What's the expected number of coin flips to get two heads in a row?**
5.  **In a room of 23 people, what's the probability two share a birthday?**

### Statistics & Hypothesis Testing

6.  **Explain the difference between Type I and Type II errors. Which is worse?**
7.  **What's a p-value? What does p = 0.05 actually mean?**
8.  **When would you use a t-test vs. a z-test?**
9.  **Explain the Central Limit Theorem. Why does it matter?**
10.  **What's the difference between correlation and causation? Give an example.**
11.  **What is statistical power? How do you increase it?**
12.  **Explain confidence intervals. What does 95% confidence actually mean?**

**Sample Answer: Explaining p-values:**

**Wrong answer:** "p-value is the probability the null hypothesis is true."

**Right answer:** "The p-value is the probability of observing data at least as extreme as what we observed, assuming the null hypothesis is true. A p-value of 0.05 means there's a 5% chance of seeing this result (or more extreme) if there's actually no effect. It does NOT tell us the probability the null is true-that requires Bayesian analysis."

#### Common Mistake

Many candidates incorrectly state that "p-value is the probability the null hypothesis is true." This fundamental misunderstanding can cost you the interview.

## Machine Learning Questions

Interviewers want to know that you understand algorithms deeply-not just how to import sklearn.

### Fundamentals

13.  **Explain the bias-variance tradeoff.**
14.  **What is regularization? Explain L1 vs L2.**
15.  **How do you handle overfitting?**
16.  **What's the difference between bagging and boosting?**
17.  **Explain gradient descent. What's the role of learning rate?**
18.  **What's cross-validation? When would you use k-fold vs. leave-one-out?**

### Algorithms

19.  **Explain how a decision tree works. How does it decide where to split?**
20.  **What's the difference between Random Forest and Gradient Boosting?**
21.  **How does logistic regression work? What assumptions does it make?**
22.  **Explain SVMs. What is the kernel trick?**
23.  **How does k-means clustering work? What are its limitations?**
24.  **Explain PCA. When would you use it?**

### Model Evaluation

25.  **When would you use accuracy vs. AUC-ROC vs. F1?**
26.  **Explain precision vs. recall. When does each matter more?**
27.  **How do you evaluate a recommendation system?**
28.  **What metrics would you use for an imbalanced classification problem?**

#### Framework: Explaining ML Algorithms

**1\. Intuition:** What's the core idea in plain English?

**2\. How it works:** Brief description of the algorithm

**3\. Assumptions:** What conditions need to be true?

**4\. Pros/Cons:** When to use it, when not to

**5\. Practical tips:** Hyperparameters, common issues

## SQL Questions

SQL is tested in almost every data science interview. Practice writing queries without auto-complete.

29.  **Find the second-highest salary from an employees table.**
30.  **Write a query to find duplicate emails in a users table.**
31.  **Calculate a 7-day rolling average of daily active users.**
32.  **Find customers who made purchases in January but not February.**
33.  **Rank employees by salary within each department.**
34.  **Find the most popular product in each category.**
35.  **Calculate month-over-month growth in revenue.**
36.  **Write a query to find gaps in sequential IDs.**

#### SQL Pattern: Window Functions

Window functions are crucial for data science SQL interviews:

**ROW\_NUMBER():** Assign unique ranks

**RANK() / DENSE\_RANK():** Handle ties differently

**LAG() / LEAD():** Access previous/next rows

**SUM() OVER():** Running totals

**AVG() OVER(ROWS BETWEEN):** Moving averages

## Python/Coding Questions

Expect to write Python code for data manipulation, sometimes on a whiteboard.

37.  **Write a function to calculate the moving average of a list.**
38.  **Given a pandas DataFrame, clean and transform messy data.**
39.  **Implement a function to calculate cosine similarity between two vectors.**
40.  **Write code to handle missing values in a dataset (explain your strategy).**
41.  **Implement k-means clustering from scratch.**

## Business Case Study Questions

These questions test whether you can apply data science to real business problems. Structure your answer clearly-interviewers are evaluating your thinking process.

42.  **How would you measure the success of Instagram Reels?**
43.  **Netflix wants to test a new recommendation algorithm. How would you design the experiment?**
44.  **Uber notices ride cancellations increased 10% last week. How would you investigate?**
45.  **How would you build a model to predict customer churn?**
46.  **Your A/B test shows the new feature increases engagement but decreases revenue. What do you do?**

#### Framework: Metrics Case Studies

**1\. Clarify the goal:** What are we optimizing for? Who's the user?

**2\. Define North Star metric:** One metric that captures success

**3\. Supporting metrics:** Guardrails and diagnostic metrics

**4\. Potential issues:** Gaming, Goodhart's Law, unintended consequences

**5\. Tradeoffs:** Short-term vs long-term, user vs business

## How to Practice

Data science interviews require broad preparation across multiple domains.

-   **SQL:** Practice on LeetCode, StrataScratch, or Mode Analytics
-   **Statistics:** Review a good stats textbook (like "Naked Statistics")
-   **ML:** Be able to explain algorithms, not just use them
-   **Coding:** Practice pandas operations without auto-complete
-   **Case studies:** Study metrics for products you use daily

The hardest part of data science interviews is communicating technical concepts clearly. You might understand the bias-variance tradeoff perfectly, but can you explain it to a non-technical PM? [MORT's Interview Practice](https://mortit.com/features/interview-practice) helps you practice explaining these concepts out loud-which is very different from knowing them in your head.

## Company-Specific Tips

-   **Google:** Heavy on statistics and experimentation. Know A/B testing deeply.
-   **Meta:** Strong SQL focus. Practice complex analytical queries.
-   **Netflix:** Product sense and metrics. Think about recommendation systems.
-   **Amazon:** Leadership principles + case studies. Prepare stories.
-   **Startups:** More practical, end-to-end projects. Show you can ship.

## Practice data science interviews with AI

MORT's Interview Practice includes data science-specific questions covering statistics, ML concepts, and case studies. Get feedback on how clearly you explain technical concepts.

[Learn About Interview Practice](https://mortit.com/features/interview-practice) [Try a Free Mock Interview](https://app.mortit.com/signup)

## More Interview Resources

### [Technical Interview Guide](https://mortit.com/blog/technical-interview-preparation-guide)

Complete preparation strategy for technical roles

### [Data Analyst Resume Example](https://mortit.com/blog/data-analyst-resume-example)

SQL, visualization skills, and business impact

### [Complete Interview Prep Guide](https://mortit.com/blog/interview-preparation-guide)

Everything you need from research to follow-up