Meta Data Scientist Interview Questions (2026)

Landing a Data Scientist role at Meta requires targeted preparation. Meta uses a structured interview process with a coding round, system design round, and behavioral round. Coding interviews focus on algorithms and data structures with emphasis on optimal solutions. System design questions test your ability to build scalable social and real-time applications. Behavioral questions focus on your ability to move fast and create impact. This guide covers the most frequently asked questions and insider tips to help you succeed in your Meta Data Scientist interview.

About the Meta Interview Process

Meta interviews combine coding assessments with system design and behavioral questions focused on impact and moving fast.

Meta uses a structured interview process with a coding round, system design round, and behavioral round. Coding interviews focus on algorithms and data structures with emphasis on optimal solutions. System design questions test your ability to build scalable social and real-time applications. Behavioral questions focus on your ability to move fast and create impact.

Why Meta Data Scientist Interviews Are Different

Meta Data Scientist interviews differ from standard Data Scientist interviews in several key ways. The company has a unique interview culture, specific evaluation criteria, and expects candidates to demonstrate alignment with their values and mission. Understanding these differences gives you a significant advantage over other candidates.

Top 10 Data Scientist Interview Questions at Meta

1. At Meta, you might be asked: Explain the bias-variance tradeoff.
2. Meta interviewers often ask: How do you handle missing data in a dataset?
3. Expect this at Meta: What is the difference between supervised and unsupervised learning?
4. At Meta, you might be asked: Describe the steps you take in a typical data science project.
5. At Meta, you might be asked: How do you evaluate the performance of a classification model?
6. A common Meta interview question: Explain regularization and when you would use it.
7. Meta candidates should prepare for: What is cross-validation and why is it important?
8. At Meta, you might be asked: How do you communicate complex findings to non-technical stakeholders?
9. Meta interviewers often ask: Describe a project where your analysis led to a significant business decision.
10. Meta candidates should prepare for: What is the difference between correlation and causation?

Meta-Specific Preparation Tips for Data Scientist Candidates

• Practice medium to hard coding problems focusing on optimal time and space complexity
• Study system design for social networks, messaging, and real-time feeds
• Prepare stories demonstrating bold decisions and moving fast with impact
• Be ready to discuss how you handle disagreements and drive consensus
• Practice coding on a shared screen or whiteboard environment

General Data Scientist Interview Tips

• Brush up on statistics and probability fundamentals
• Practice coding in Python or R with real datasets
• Prepare to explain complex models in simple terms
• Have portfolio projects that demonstrate end-to-end data science work

Preparation Timeline for Meta Data Scientist Interviews

• 4 weeks before: Research Meta culture, recent news, and the specific team you are applying to.
• 2-3 weeks before: Practice technical questions daily and prepare behavioral stories using the STAR method.
• 1 week before: Do full mock interviews with HireFlow AI simulating Meta interview style.
• Day before: Review your notes, prepare questions for the interviewer, and get a good night of rest.

Practice Meta Data Scientist Interview with HireFlow AI — our AI adapts to Meta's interview style and gives real-time feedback.