Data Science Interview Prep (5 steps to a FAANG+ offer)

Data science interviews have changed significantly in recent years. While they traditionally focused on statistics, machine learning fundamentals, coding, and product sense, many companies now also test ML system design and your ability to reason through AI-related risks and trade-offs.

This updated guide is designed to equip you with everything you need to succeed in your data science interview preparation. Step by step, we’ll show you exactly how to prepare, share expert insights, and point you to top free resources.

We’ve coached hundreds of data scientists for their interviews at top tech companies, such as Meta, Google, and Amazon. Now, we’ve distilled everything into five key steps to help you do the same.

• Step 1: Research your target company
• Step 2: Prepare for all data scientist question types
• Step 3: Deep-dive (links to top DS resources)
• Step 4: Do free mock interviews
• Step 5: Get feedback from experts

Ready? Let's go.

Click here to practice 1-on-1 with FAANG ex-interviewers

Step 1: Research your target company

Whether you're interviewing at FAANG, emerging tech companies, AI labs, or a start-up, your interviewers will expect you to thoroughly research the company.

Familiarize yourself with the company's mission statement, core values, and the range of products and services they offer. Highlight how your work can contribute to their goals. 

Understanding the company’s product is especially important for data scientists because the product is ultimately the source of the data. 

Product questions often come up in case study interviews. An interviewer might ask how you would measure the success of a new feature. “If you don't understand their revenue model, your answer will be technically sound but strategically useless,” says Hanif.

In addition to that, you also need to know that different companies handle the data science interview process differently. 

For example, in an Amazon technical screen, both your technical skills and alignment with their Leadership Principles are equally evaluated. While other companies like Google or Meta focus solely on technical skills at this step in the process.

For more AI-focused companies like TikTok or OpenAI, you may also see questions around data quality and governance, especially how data is collected, validated, and used responsibly in AI systems.

To help you, we've put together the free company guides below, which go into detail on each company's interview process and how to best prepare for it.

• Google data scientist interview guide
• Meta data scientist interview guide
• Amazon data scientist interview guide
• TikTok data scientist interview guide
• Uber data scientist interview guide
• OpenAI data scientist interview guide

Not applying to the companies listed above? No problem – this guide is still 100% relevant if you're interviewing at a large company with a structured hiring process.

For smaller startups, the interview format can vary, but the topics we cover here are still your best bet for preparation.

Step 2: Prepare for all data scientist question types ↑

Data science interviews are tough because they cover a wide range of question types.

To make prep easier, we’ve broken them down into five key categories, showing the frequency at which they appeared in the 300+ questions we’ve analyzed from leading tech companies, including Google, Meta, Amazon, TikTok, and Uber.

You’ll also find insights from Hanif, former Senior Data Science Manager at Amazon and Meta, who has conducted 230+ interviews as a hiring manager and Bar Raiser, and coached 150+ candidates for top tech roles.

Below, you’ll find an overview of each category. Click the links to dive deeper. They'll take you to specific guides, complete with answer frameworks and real example questions. Feel free to skip to a specific section.

• Coding questions
• Statistics questions
• Machine learning questions
• Product sense questions 
• Behavioral / resume questions

Note that many of these questions come in the form of case studies. For more information on what to expect and how to prepare for these types of questions, see our data science case interview guide.

Ready? Let’s get started.

2.1 Coding questions ↑

The coding questions you'll get in data science interviews will test your problem-solving and data manipulation skills through the following areas: 

• SQL – writing queries to extract, aggregate, and analyze data
• Data structures and algorithms (DSA) – solving algorithmic problems using efficient data structures
• Modeling – selecting, evaluating, and improving models for real-world problems
• Statistical coding (Google only) – implementing statistical methods and simulations in code

The types of coding questions you’ll get will vary by company. For instance, Meta and TikTok data scientist interviews focus largely on SQL. At Google, you’ll see more statistical coding questions that require Python or R. You should also be prepared to solve data structure and algorithm questions, which are most commonly done in Python.

Some companies like Meta are also incorporating AI-assisted coding into their interviews. Here, candidates are given the option to use AI assistance while solving a multi-stage problem and are asked to explain their reasoning, trade-offs, and how their solution fits into the larger system.

You’ll usually be coding on a whiteboard or a virtual equivalent, so practice writing queries and scripts by hand or on a plain text editor while explaining your reasoning. In some interviews, such as at Amazon, coding rounds were previously reported to be entirely verbal.

“Coding rounds are often about ‘interrogating’ the data,” says Hanif. This means interviewers want to see how you explore datasets, ask clarifying questions, identify patterns or anomalies, and investigate them systematically using code or SQL.

Interviewers also look for proficiency with tools like Pandas and SQL, including window functions and complex joins, as well as the ability to write clean, reproducible scripts for modeling.

Here are some great resources for preparing for coding questions.

• Read:
  • How to prepare for FAANG coding interviews (by IGotAnOffer)
  • Analysis of the 3 types of SQL problems (by Towards Data Science)
  • SQL Course
  • Mode Analytics SQL Tutorials
  • Programmer Interview SQL Practice Database
  • Python | SQL Comparison
  • Data Transformations in R
• Watch:
  • How to answer programming questions (by Amazon)
  • Practice for 3 types of SQL interviews (by Emma Ding)
• Practice:
  • Practice SQL and programming with medium and hard-level examples on LeetCode, HackerRank, or StrataScratch 

2.2 Statistics questions ↑

Data scientists need to derive insights from large and complex datasets, which makes statistical analysis a core part of the role. At companies that emphasize machine learning, it also plays a key role in preparing and organizing data for model development.

Before your interviews, take time to review key statistics fundamentals and practice explaining concepts clearly (e.g., p-value, recall). You’ll also commonly see questions on A/B testing, probability, and statistical models. At Google, this is one of the most common question types.

Strong candidates don’t just memorize statistical definitions; they demonstrate intuition. 

“Interviewers want to see that you understand experimentation, real-world data issues like bias or network effects, and when to apply different statistical distributions based on the problem,” says Hanif, ex-Amazon Sr. DS Manager.

Here are some great resources for preparing for statistics questions.

Read:

• Khan Academy Statistics & Probability Course
• StackExchange for questions and answers around statistics, machine learning, data analysis, etc.
• Reddit’s statistics threads to discuss questions with peers

Watch:

• StatQuest with Josh Starmer for visual breakdowns of statistical concepts (p-values, distributions, math behind linear models, etc.) 

2.3 Machine learning questions ↑

Data scientists must develop services and solve problems that are endlessly complex and constantly evolving. So your interviewer will test your ability to build innovative algorithms that improve and remain accurate over time.

Most companies assess both machine learning breadth and depth in these interviews. Breadth refers to your familiarity with a range of models and concepts, and knowing when to use them. Depth refers to how well you understand those models, including how they work, how to evaluate them, and how to improve them.

Depending on the role, you may be asked to discuss ML concepts or walk through how you would approach a modeling problem based on real-life business decisions (more common at Google and Amazon). 

According to Hanif, here are some essential topics that you need to brush up on:

• Feature engineering: Creating and selecting meaningful features, often more impactful than the model choice itself.
• Evaluation metrics: Choosing the right metric (e.g., Precision/Recall, F1-score, ROC-AUC) based on the business impact of false positives vs. false negatives.
• Overfitting and regularization: Understanding techniques like L1 (Lasso) and L2 (Ridge) regularization, and when to use each to improve generalization.

In some roles, especially at mid to senior levels, you may also see ML system design interviews. These focus on how models are deployed and scaled in production, including data pipelines, inference, monitoring, and reliability.

“For ML, depth usually beats breadth,” says Hanif. Focus on deeply understanding a few algorithms rather than many at a surface level. Be ready to explain concepts like loss functions, handling outliers, and bias-variance tradeoffs.

Let's look at some sample questions.

Here are some great resources for preparing for machine learning questions.

Read:

• ML system design interviews by IGotAnOffer for a breakdown of the different ML concepts you need to master
• Reddit’s machine learning threads to discuss questions with peers
• StackExchange for machine learning Q&A
• Kaggle courses for introductory and intermediate ML, data cleaning, visualization, SQL, and more
• Facebook’s machine learning field guide for an end-to-end process for implementing machine learning solutions

Watch:

• StatQuest with Josh Starmer for breaking down complex ML algorithms (XGBoost, Random Forests, Neural Networks, etc.)

2.4 Product sense questions ↑

In addition to the skills mentioned above, data scientists help to drive product and business decisions. They use data to generate insights that help improve products and support growth.

Be prepared to apply your technical knowledge to business scenarios. Product sense questions often show up differently depending on the company. For example: 

• Google often asks questions involving statistical A/B testing to evaluate product performance
• Meta tends to focus on metrics (e.g., how to set good metrics, how to react to metric changes)
• Amazon asks very few questions about product sense
• Uber tests whether you understand products from both user and business angles
• TikTok tests whether you understand what makes a feature valuable and how it fits into its broader strategy

Let's look at some sample questions. 

Here are some great resources for preparing for product sense questions. Some are intended for PM interviews, but you might pick up a few tips and tricks you can apply to your DS interviews:

• Read:
  • How to crack product sense interviews (by IGotAnOffer)
  • How to crack product metric interviews (by IGotAnOffer)
  • How to crack product improvement interviews (by IGotAnOffer)
  • Favorite product interview question (by IGotAnOffer)
• Watch:
  • Ex-FAANG interviewer on how to answer Meta product sense interviews (by IGotAnOffer)
  • Ex-Google interviewer explains how to answer AI product sense interviews (by IGotAnOffer)

2.5 Behavioral / resume questions ↑

Finally, you can expect behavioral or “resume” questions about your past experience and your motivation for applying. Interviewers are looking for strong communication skills and a clear fit with the company’s culture.

Be strategic by aligning your answers with the key qualifications listed in the job description. If the company has published core values, review them and prepare a story bank with examples from your experience that reflect each one. This is especially important at companies like Amazon, where the Leadership Principles are integrated into almost every stage of the interview process.

For senior and leadership positions, these are often the make-or-break soft skills interviewers look for, according to Hanif:

• Data storytelling: Explaining complex analyses and models clearly to non-technical stakeholders
• Prioritization: Identifying which projects or initiatives will drive the greatest business impact
• Adaptability: Adjusting your approach when data disproves a hypothesis instead of forcing a conclusion
• Cross-functional collaboration: Working closely with product and engineering teams as a strategic partner, not just a service provider

Here are some great resources for preparing for behavioral interview questions. Again, some of these were made for PM roles, but apply just as well to data science interviews.

• Read:
  • Most common behavioral questions (by IGotAnOffer, includes method, tips, and example answers)
  • Leadership primer for tech interviews (with Nupur D, overview of leadership topics and how to talk about them)
• Watch:
  • FAANG interviewers answer "Tell me about yourself" (by IGotAnOffer)
  • Ex-Google hiring manager on how to create a story bank for behavioral interviews (by IGotAnOffer)
  • How to ace the STAR method (by Amazon)

Step 3: Deep-dive into more resources ↑

The resources above give you a solid foundation on key data science topics, but to truly master them, you'll need to go deeper. 

Here are some deep-dive resources that you may find useful. Note that some of the items below are not about DS interviews, but nevertheless, it can be worthwhile to immerse yourself in high-quality content around data science ahead of your interview.

Read

• Ace the Data Science Interview (Singh and Huo). Nick Singh and Kevin Huo both worked for Facebook and Google; their book is a comprehensive guide covering statistics, probability, machine learning, and SQL with over 200 real industry questions.
• An Introduction to Statistical Learning (James, Witten, Hastie, and Tibshirani). Often referred to as the "Bible of ML," this book provides an accessible overview of the field with a focus on the intuition behind the algorithms. The Python version (ISLP) is particularly useful for modern candidates.
• Designing Machine Learning Systems (Chip Huyen). Chip Huyen is a computer scientist at Stanford and a co-founder of Claypot AI. This book is the gold standard for learning how to design end-to-end ML systems that are reliable, scalable, and maintainable in production environments.
• How to crack data science case studies (by IGotAnOffer): A definitive guide to navigating open-ended interview scenarios, focusing on how to break down ambiguous business problems into structured, data-driven solutions.
• Frameworks for answering business case questions: A deep dive into specialized structures for product analytics, covering root cause analysis for KPIs, defining success metrics (AARRR), measuring feature adoption, and the end-to-end steps for designing a rigorous A/B test.

Watch

• Andrej Karpathy. For candidates looking to master Deep Learning and LLMs, Karpathy (founding member of OpenAI and former Director of AI at Tesla) provides "Zero to Hero" deep dives that explain modern AI architecture line-by-line.
• Krish Naik. A fantastic resource for end-to-end industry projects. His channel is particularly useful for understanding how to move beyond model training into deployment, MLOps, and real-world problem scenarios.

Listen

• The Artists of Data Science. Hosted by Harpreet Sahota, this podcast focuses on personal growth and career development within the field. Its "Office Hours" episodes are particularly valuable, as they allow you to hear real questions from other data scientists or sign up to ask your own.
• Super Data Science. A high-energy podcast that covers everything from technical tutorials on the latest algorithms to broader industry trends and career advice.
• Data Skeptic. This long-running podcast uses short, focused episodes to explain high-level concepts like k-means clustering or p-hacking through relatable analogies and stories.

By the way, we have zero affiliate relationships with the third-party resources above, they're just our independent recommendations!

Step 4: Do free mock interviews ↑

Learning the question types and the specific interview process for your favorite company will go a long way in helping you prepare. 

And practicing with the right resources will take you further. But it's not enough to land you a data scientist job offer. 

To succeed in your DS interviews, you're also going to need to practice under realistic interview conditions so that you'll be ready to perform when it counts. 

The easiest way to start practicing under simulated interview conditions is to practice interview questions out loud or with peers.

Doing mocks with peers can be very worthwhile, but be warned, you may come up against the following problems:

• It’s hard to know if the feedback you get is accurate
• They’re unlikely to have insider knowledge of interviews at your target company
• On peer platforms, people often waste your time by not showing up

For those reasons, many candidates skip peer mock interviews and go straight to mock interviews with an expert.

Step 5: Get feedback from experts ↑

In our experience, practicing real interviews with data science interview coaches makes a huge difference, as it allows you to:

• Test yourself under real interview conditions
• Get accurate feedback from a real expert
• Build your confidence
• Get company-specific insights
• Learn how to tell the right stories better
• Save time by focusing your preparation

Landing a job at a big tech company often results in a $50,000 per year or more increase in total compensation. In our experience, three or four coaching sessions worth ~$500 make a significant difference in your ability to land the job. That’s an ROI of 100x!

Click here to book data science mock interviews with experienced data science interviewers.