Data Science Interview Prep (2025 Guide)

Data Science Interview Prep (2025 Guide) was originally published on Exponent.

This is a breakdown of data science interviews and how to prepare for them.

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Verified: We’ve distilled the core lessons from hundreds of hours of interview prep with top-performing candidates, hiring managers, and senior data scientists at companies like Meta, Amazon, and DoorDash.

This guide focuses on what interviewers are looking for: not just technical correctness, but your ability to reason through ambiguity, communicate clearly, and tie your work to business outcomes.

By the end of this guide, you’ll have a high-level framework for approaching every round with clarity, confidence, and structure.

Data Science Roles Explained

Not all data science interviews are created equal.

The questions you’ll face and the skills you need to highlight depend heavily on the specific flavor of data science role you’re targeting.

First, understand what kind of data scientist the company is hiring for.

Role
Focus
Skills
Examples

Machine Learning
Building and tuning ML models and systems.
– ML systems at scale
– Algorithm tuning
– Model vs. business performance
Airbnb: Build scalable ML models and pipelines
NVIDIA: Implement algorithms for large-scale projects
Thumbtack: Deploy ML systems
TikTok: AI/ML including NLP, CV, audio processing

Product Analytics
Driving business decisions and experiments using data
– SQL
– Product sense
– Business metrics
– Experimentation
Doordash: Build analytics experiments and dashboards
Meta: Measure product success with metrics
Waymo: Track health of commercial products and run experiments

Full Stack
Combination of ML, product, and statistical analysis
– Causal inference
– EDA
– Statistical models
– Hypothesis testing
Walmart: Design interventions with statistical methods
Grammarly: Run SEO experiments with causal inference
Google: Apply statistical methods to product development

Engineering
Preparing and processing large-scale data for others
– Big data tech (Spark)
– Batch pipelines
– Scala/Python
Netflix: Build systems to process and model data
LinkedIn: Build performant systems for massive-scale analysis

Here are the four most common role types:

Machine Learning-Focused

These roles expect you to design, tune, and sometimes productionize ML models.

You’ll see fewer business metric questions and more deep dives into algorithms, pipelines, and model evaluation.

Interview focus:

  • ML coding (e.g., implement model from scratch, tune hyperparameters)
  • ML concepts (e.g., pros/cons of XGBoost vs. logistic regression)
  • Data preprocessing and feature engineering
  • Occasional deep learning or NLP if the team focuses on those areas

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Common job titles: Applied Scientist, ML Data Scientist, AI Researcher

Product/Analytics-Focused

These are closer to product manager or business analyst roles, focusing on generating insights, influencing decisions, and driving product growth through data.

Interview focus:

  • SQL and experimentation (e.g., A/B testing)
  • Product sense and business metrics
  • Communication and stakeholder management
  • Less emphasis on advanced ML algorithms

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Common job titles: Data Scientist, Product Analyst, Business Data Scientist

Full Stack Data Scientist

These roles require strong ML chops and a solid business and product strategy.

You’re expected to own projects end-to-end, from defining metrics to deploying models and analyzing impact.

Interview focus:

  • ML coding + experimentation + product intuition
  • Strong statistics foundation
  • Communication across tech and business stakeholders

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Common job titles: Full-Stack Data Scientist, Generalist DS

Rubric for Stats and Experimentation Data Science Interviews

Data Engineering-Focused

Not a traditional DS role, but some job titles overlap. Data engineering roles are more focused on infrastructure, pipelines, and tooling.

Interview focus:

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Common job titles: Data Scientist – Platform, Data Engineer, ML Engineer

Read the job description closely.

Your prep should lean analytical if it emphasizes A/B tests, SQL, and metrics. If it calls for building pipelines and tuning models, go deeper on ML and systems.

Interview Process

While the exact process varies by company and role type, here’s a typical breakdown of what to expect:

Data Science Interview Loop

Recruiter Screen

Approximately 30 minutes.

This is a quick fit check. The recruiter will:

  • Walk through the job scope
  • Ask about your background and salary expectations
  • Outline the interview process and timeline

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Tip: Be clear about your role preferences (analytics, ML, etc.) and ask questions to clarify expectations early.

Technical Screen

Approximately 30-60 minutes.

You’ll face 2–4 short questions, usually around:

  • SQL
  • Basic statistics or probability
  • Python fundamentals
  • Lightweight ML concepts

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Tip: Treat this like a pass/fail filter. Practice clean, efficient code and explain your reasoning clearly.

Statistics & Experimentation

Approximately 60 minutes.

It is one of the most common and heavily weighted rounds for analytics and product-focused roles.

You may be asked to:

  • Design an A/B test from scratch
  • Walk through a hypothesis test
  • Discuss statistical assumptions and pitfalls
  • Calculate power or confidence intervals

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Tip: Practice structured thinking. Clarify the problem, define metrics, state hypotheses, and reason through edge cases.

SQL

Approximately 60 minutes.

The SQL round tests your ability to manipulate data directly, often from 1–2 tables with joins, filters, and aggregations.

Expect to:

  • Use GROUP BY, WINDOW FUNCTIONS, CASE
  • Explain your query logic
  • Interpret or debug a provided query

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Tip: Write readable, well-indented queries and focus on correctness and performance.

ML Coding

Approximately 60 minutes.

You’ll be asked to code up and evaluate a small ML model, typically in Python.

Think of real-world scenarios like churn prediction, fraud detection, or personalization.

How to Answer ML Coding Interview Questions

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Tip: Focus on structured pipelines: data prep → model → evaluation. Use libraries you’re most comfortable with (e.g., scikit-learn).

Machine Learning Concepts

Approximately 60 minutes.

This round explores your understanding of key ML algorithms and trade-offs. (e.g., linear regression, decision trees, KNN)

Common questions:

  • “How does random forest work?”
  • “What’s your favorite algorithm and why?”
  • “How would you improve a model with high variance?”

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Tip: Use examples from past projects and explain trade-offs like a teacher, not a textbook.

Product Sense & Case Study

Approximately 45-60 minutes.

Primarily for analytics-focused roles, these rounds mimic the product management interview. You’ll be expected to:

  • Define key product metrics
  • Suggest experiments or KPIs
  • Evaluate product impact from a dataset

Read more: Case Study Interviews for Data Scientists

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Tip: Practice structured responses using mini case studies (e.g., “How would you measure the success of a new feature?“).

Behavioral

Approximately 30-60 minutes.

Data science behavioral rounds test collaboration, leadership, and how you communicate technical work.

Expect questions like:

  • “Tell me about a time you had to influence without authority”
  • “Describe a project you led from start to finish”
  • “How do you handle stakeholder pushback?”

📌 Prep tip: Use a consistent story format (e.g. STAR), but tailor stories to the company’s values and goals.

Take-Home Assignment (2–5 hours)

Approximately 2-5 hours.

Take-home assignments are common at startups or early-stage teams.

You’ll be asked to analyze a dataset and present findings. Sometimes open-ended (“Find something interesting”), other times structured.

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Tip: Structure your deliverable like a business report: start with your recommendation, not your code.