35 Data Scientist Interview Questions (2025) + Practice with AI

Master the STAR Method

Structure answers with Situation, Task, Action, Result. Emphasize the problem you solved (20%), the analytical approach and models used (40%), implementation details (20%), and quantified business impact (20%). Always include metrics and statistical rigor.

Essential DS Frameworks

ML Model Selection: Linear → Tree-based → Ensemble → Deep Learning progression

Feature Engineering: Domain knowledge → Transformation → Selection → Validation

Model Evaluation: Accuracy vs Precision/Recall vs F1 vs ROC-AUC based on problem

Experimental Design: Hypothesis → Sample Size → Test Design → Statistical Significance

DS Workflow: Problem Definition → EDA → Feature Engineering → Modeling → Evaluation → Deployment

Interview Process Timeline

Phone Screen (45-60 min): ML fundamentals, statistics, SQL/Python coding basics

Technical Round 1 (60 min): ML algorithms deep-dive, model selection and evaluation

Technical Round 2 (60 min): Take-home case study or live coding with data analysis

Technical Round 3 (60 min): System design for ML, A/B testing, experimentation

Behavioral Round (45 min): Cross-functional collaboration, stakeholder communication

Common Mistakes to Avoid

Using complex models without justifying why simpler approaches won't work

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

Master Your Data Science Interview

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Data Scientist Interview Overview

Don't Bomb Your Data Scientist Interview

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.

How to Prepare for Your Data Scientist Interview

Master the STAR Method

Essential DS Frameworks

Interview Process Timeline

Common Mistakes to Avoid

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

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Resources for Data Scientist Interviews

STAR Method Interview Guide - Complete Framework with 20+ Examples

Tell Me About Your Greatest Accomplishment - Why 70% Undersell Themselves (And How to Get It Right)

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