NewAI powered Interview Practice

Master Your Data Science Interview

Get personalized feedback on your statistical analysis, machine learning knowledge, and data modeling capabilities

Last updated: November 20, 2025

Data Science interviews at top tech companies typically include statistics fundamentals, machine learning concepts, coding challenges in Python/SQL, case studies, and product sense questions. You'll be expected to explain complex technical concepts clearly, demonstrate business impact thinking, and show hands-on experience with real-world data problems. Strong foundation in probability, statistics, and ML algorithms is crucial.

Common Data Scientist Interview Questions

Review these questions below, or use our AI to practice your answers instantly.

Want personalized feedback on your answers? Start practicing with AI.

Key Data Scientist Interview Topics

Common topics and questions you might encounter in your Data Scientist interview

Statistical Analysis

Hypothesis Testing
Probability Theory
Experimental Design
Statistical Modeling
A/B Testing

Machine Learning

Supervised Learning
Unsupervised Learning
Model Evaluation
Feature Engineering
Deep Learning

Data Processing

Data Cleaning
ETL Pipelines
Data Visualization
Big Data Tools
Data Quality

Revarta helps you ace your interviews

Built with extensive experience - conducting interviews and passing interviews at Google, NVIDIA, Amazon, Adobe and Remitly

Why Practice Data Scientist Interviews with Revarta

Benefit:Real-World Analysis

Practice with actual data science challenges and statistical problems faced in tech interviews

Benefit:Identify your gaps

Personalized questions based on your analytical expertise and modeling skills let you immediately discover areas you need to improve on

Benefit:Master problem-solving

Strengthen your responses by practicing areas you're weak in

Benefit:Bite sized practice

Only have 5 minutes? Practice a quick statistical problem or ML concept

How we increase your success rate

Speak Your Answers

Practice interview questions by speaking out loud (not typing). Hit record and start speaking your answers naturally.

AI Listens & Analyzes

Our system processes your audio responses in real-time, transcribing and analyzing your performance.

Get Instant Feedback

Receive detailed analysis and improved answer suggestions. See exactly what's holding you back and how to fix it.

FAQ

How does Revarta work?

Revarta conducts live audio interviews where you speak your answers out loud, just like in a real interview. We use your resume, desired job, and company profile to generate relevant questions. Your spoken responses are recorded, transcribed, and analyzed to provide personalized feedback and answer improvements. It's practice that mirrors real interviews - speaking out loud, getting instant feedback, and improving with every session.Read more →

How does the app conduct live audio interviews?

Using your phone or laptop, you answer interview questions by speaking out loud - not typing. The app records your audio responses, transcribes them, and generates personalized feedback based on your actual speaking performance. It's designed to simulate real interview pressure while giving you the safety to practice and improve.Read more →

What types of interviews can I practice?

Revarta specializes in behavioral interviews, which make up the majority of screening and final round questions. We support 80+ job roles including product management, software engineering, consulting, finance, and more. We also cover case study interviews and technical discussion questions. Our question library is constantly expanding based on current interview trends.Read more →

How personalized is the interview experience?

Highly personalized. We use your resume, target job, company profile, and required skills to generate relevant questions. You can even choose different interviewer personas (recruiter, hiring manager, technical lead) to practice different interview stages. Your answers are analyzed for personalized feedback and improvement recommendations specific to your situation.Read more →

How can I practice specific questions?

Yes, You can practice specific questions by using the free form practice questions to find questions that are particularly challenging or of interest to you.Read more →

How is the quality compared to human interviewers?

Our AI is trained on thousands of real interviews conducted by experienced hiring managers. While human coaches excel at strategic career advice, Revarta excels at providing consistent, bias-free feedback on your delivery and content. Most users find the quality comparable to professional interview coaches—without the $200/hour price tag.Read more →

Can I use the app on mobile devices or is it desktop only?

Revarta works on both mobile and desktop. Practice from your phone during your commute or from your laptop at home. Most users practice on mobile for convenience.Read more →

Do I need to commit to a subscription?

No. We offer flexible options: monthly plans with unlimited practice, 1-year plans with unlimited practice, or one-time payment for unlimited lifetime access. You're never locked into long-term commitments.Read more →

Do you offer a free trial?

Yes! We offer a 7-day free trial with unlimited practice, automatically activated for all new users. You can test out all features without entering your credit card. In 7 days, you'll discover whether voice-based practice builds the confidence you need for your upcoming interviews.Read more →

NewAI powered Interview Practice

Master Your Data Science Interview

Get personalized feedback on your statistical analysis, machine learning knowledge, and data modeling capabilities

Data Scientist Interview Overview

Common Data Scientist Interview Questions

Explain the difference between Type I and Type II errors. When would you prioritize minimizing one over the other?

How would you explain the Central Limit Theorem to a non-technical stakeholder? Why does it matter?

You flip a coin 100 times and get 60 heads. Is the coin fair? Walk me through your analysis.

Explain the difference between correlation and causation. How would you establish causality?

What is p-hacking and how do you avoid it in your analysis?

Explain Bayes' Theorem and give a practical application in data science.

How would you design an A/B test for a new feature? What sample size would you need?

What assumptions does linear regression make? How do you validate them?

Explain the bias-variance tradeoff and how it relates to overfitting and underfitting.

When would you use a decision tree vs a random forest vs gradient boosting?

How do you handle imbalanced datasets in classification problems?

Explain the difference between L1 and L2 regularization. When would you use each?

How would you evaluate a recommender system? What metrics would you use?

Explain how gradient descent works. What are the differences between batch, mini-batch, and stochastic gradient descent?

What is the curse of dimensionality and how does it affect machine learning models?

Explain how k-means clustering works. What are its limitations?

How do you prevent overfitting in neural networks?

What is the difference between bagging and boosting? Give examples of each.

Write a SQL query to find the second highest salary from an Employee table.

How would you detect and handle outliers in a dataset?

Explain different types of joins in SQL and when you would use each.

How do you handle missing data in a dataset? What are the trade-offs of different approaches?

What's the difference between normalization and standardization? When would you use each?

How would you design an ETL pipeline for processing large-scale data?

What techniques do you use for feature engineering? Give examples from past projects.

How do you handle categorical variables with high cardinality?

Explain precision, recall, and F1 score. When would you optimize for one over the others?

How do you choose between models with similar performance? What other factors do you consider?

What is cross-validation and why is it important? Describe different types.

How do you communicate technical findings to non-technical stakeholders?

Describe a time when your data analysis led to a business decision. What was the impact?

How do you prioritize multiple data science projects with competing deadlines?

A stakeholder disagrees with your model's results. How do you handle it?

Explain the difference between list comprehension and generator expressions in Python. When would you use each?

How do you optimize Python code that's running slowly? What profiling tools do you use?

Explain the difference between deep copy and shallow copy in Python. When does it matter?

[Google] Design a system to predict which search queries will have ads clicked. What features and model would you use?

[Google] How would you improve YouTube's video recommendation algorithm?

[Meta] How would you measure the impact of a new feature on user engagement across Facebook?

[Meta] Design a model to detect fake accounts on Instagram. What signals would you use?

[Amazon] How would you improve Amazon's product search ranking algorithm?

[Amazon] Estimate the revenue impact of improving recommendation CTR by 1% on Amazon's homepage.

Key Data Scientist Interview Topics

Statistical Analysis

Machine Learning

Data Processing

Revarta helps you ace your interviews

Why Practice Data Scientist Interviews with Revarta

Benefit:Real-World Analysis

Benefit:Identify your gaps

Benefit:Master problem-solving

Benefit:Bite sized practice

How we increase your success rate

Speak Your Answers

AI Listens & Analyzes

Get Instant Feedback

FAQ

Programming Skills

Business Acumen

Best Practices

Benefit:Judgement free

Benefit:Practice anytime

Repeat & Improve

Go at it again