Top 35 Questions to Expect in a Meta Data Science Interview in 2025

This guide covers Meta's data science interview process, key topics, and tips for success.

Landing a data science role at Meta requires thorough preparation, and interviews here are known for their comprehensive evaluation of technical expertise, business insights, and problem-solving abilities. The following guide will walk you through 35 key questions to expect in a Meta data science interview, along with a detailed breakdown of the interview process.
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Interview Process at Meta

Here’s a brief overview of the typical Meta Data Science Interview Process:

1. Phone Interview

The first step is usually a phone interview. This is meant to check if you're a good fit for the role and to test some of your technical skills. You can expect to answer questions about how you’ve worked with data before, especially focusing on tools like SQL (used for working with data) and basic data analysis. They may ask you to solve a problem using SQL or to explain a project where you used data to find important information.

2. On-Site or Virtual Interviews

If you pass the phone interview, you’ll move on to several more interviews, which could happen in person or online. These interviews cover different areas:
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Coding: You’ll likely have to write some code, often related to data handling or problem-solving.
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Statistics: They will test your knowledge of statistics, which is important for understanding data trends and making predictions.
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Product Understanding: You’ll need to show that you understand how data can help improve Meta’s products.
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Machine Learning: They may ask about machine learning, which is about teaching computers to make decisions from data.
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Business Problem Solving: In some interviews, you’ll discuss real-world business problems and how data can be used to solve them.

3. Case Study

During the interview process, you might be given a case study. This is where you are given a set of data and a business problem to solve. You’ll need to look at the data, figure out what it’s telling you, and come up with ideas on how to fix or improve the business problem based on that data.

4. Final Interview

In the last round of interviews, they’ll ask more advanced technical questions. You may also be asked to dive deep into a particular business issue, using your data skills to come up with solutions. Additionally, they’ll want to see if you fit with Meta’s mission and values, making sure you would be a good match for the company culture.
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2. Data Science Interview Questions to expect at Meta

2.1. Technical Skills

These questions test your ability to manipulate and analyze data using SQL, Python, and other tools.
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Write a SQL query to find the top-selling product of the month.
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What are window functions, and how can they be applied in SQL queries?
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Explain how you would optimize a SQL query for performance.
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How do you handle missing values in a dataset in Python?
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What is the difference between an INNER JOIN and an OUTER JOIN in SQL?

How to Learn:
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Courses: Consider enrolling in online platforms like Coursera’s Machine Learning by Andrew Ng for foundational understanding.
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Books: "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron covers topics such as overfitting and regularization in-depth.
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Practice: Implement algorithms on platforms like Kaggle using Python libraries such as Scikit-learn and TensorFlow.
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2.2. Statistical Concepts

Meta data scientists frequently deal with statistical testing and analysis, making these foundational questions vital.
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What is a p-value, and how is it used in hypothesis testing?
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How would you design an A/B test for a new feature on Instagram?
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What is statistical power, and why is it important in experiments?
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How do you handle multicollinearity in a regression model?
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Explain the Central Limit Theorem and its importance in data science.
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How to Learn:
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Courses: Take the Statistics for Data Science with Python on Coursera or edX’s Probability and Statistics.
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Books: "Statistics for Data Scientists" by Peter Bruce is a good starting point for practical applications.
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Practice: Engage in solving real-world statistical problems on platforms like DataCamp or Khan Academy.
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2.3. Machine Learning
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Be prepared for questions on machine learning concepts, algorithms, and their practical implementation in real-world scenarios.

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What is the bias-variance tradeoff, and why is it important?
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How does regularization help prevent overfitting in a model?
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How do you evaluate a classification model's performance?
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What is cross-validation, and why is it important in machine learning?
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Explain how decision trees work, and when you would use them.
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How to Learn:
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Courses: Explore Advanced Machine Learning Specialization by Coursera or Fast.ai for hands-on algorithm learning.
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Books: "Machine Learning Yearning" by Andrew Ng helps understand when to use different models.
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Practice: Kaggle has great datasets for practicing Random Forest and XGBoost applications.

2.4. Product Sense

Meta expects its data scientists to have a strong understanding of product metrics and business impact. These questions test your ability to think about data in the context of product development.
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How would you measure the success of a new feature on Facebook?
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What product metrics would you focus on to improve engagement on Instagram Stories?
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If WhatsApp usage drops by 20%, how would you investigate the root cause?
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What KPIs would you prioritize when launching a new Meta product?
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How would you design a data-driven strategy to increase Facebook Marketplace adoption?

How to Learn:
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Courses: Take the Data Wrangling in Pandas course on Udemy.
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Books: "Python for Data Analysis" by Wes McKinney is a great resource for data wrangling techniques.
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Practice: Use platforms like Kaggle or DrivenData for real-world data cleaning challenges.
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2.5. Behavioral Questions

In addition to technical expertise, Meta evaluates cultural fit and collaboration skills. Expect behavioral questions that gauge your adaptability, teamwork, and communication skills.
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Tell me about a time when you worked with a cross-functional team to solve a problem.
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Describe a situation where you faced a challenge in data collection. How did you handle it?
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How do you prioritize multiple data science projects with competing deadlines?
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Describe a time when you disagreed with a colleague on how to approach a project.
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How do you handle feedback from team members and managers?
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How to Learn:
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Books: "Cracking the Data Science Interview" by Maverick Lin offers great behavioral interview prep tips.
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Articles: Read case studies on sites like Towards Data Science or Medium that cover real-world project management.
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Mock Interviews: Practice behavioral questions on platforms like Pramp or Interviewing.io.
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2.6. Advanced Machine Learning & Deep Learning

With the growing importance of AI, Meta’s data scientists need to understand complex machine learning models, deep learning, and cutting-edge algorithms.
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Explain how a convolutional neural network (CNN) works and its applications.
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What is a recurrent neural network (RNN), and how is it different from a CNN?
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How would you implement transfer learning in a machine learning model?
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What is the role of dropout in a neural network, and why is it important?
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Explain how natural language processing (NLP) is applied at Meta.
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How to Learn:
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Courses: Enroll in Data Visualization with Python on Coursera or DataCamp.
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Books: "Storytelling with Data" by Cole Nussbaumer Knaflic is a must-read for learning how to visualize data effectively.
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Tools: Practice using Tableau, PowerBI, or Matplotlib for visualizations on Kaggle.

2.7. Analytical Thinking & Problem Solving
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Meta data scientists are expected to think critically and solve complex business problems. These questions assess your analytical reasoning.
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How would you forecast daily active users for Facebook for the next year?
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How would you diagnose a sudden drop in Facebook Ad engagement?
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If Instagram engagement decreased by 10%, how would you analyze the cause?
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How would you balance product growth and user satisfaction in your analysis?
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How do you handle incomplete or noisy data when making business recommendations?

How to Learn:
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Courses: Try the Deep Learning Specialization by Andrew Ng on Coursera.
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Books: "Deep Learning" by Ian Goodfellow is a deep dive into neural network architectures.
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Practice: Use frameworks like TensorFlow and PyTorch to implement neural networks on Kaggle or Google Colab.

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Conclusion

Meta’s data science interviews will continue to evolve as the company pushes the boundaries of machine learning, AI, and big data. The questions outlined here provide a strong foundation to start your preparation for 2025.

For even deeper preparation, you can check out Ace the Data Science Interview, which offers more comprehensive insights into technical and behavioral data science interviews.

Prepare well, and good luck with your Meta data science interview!

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