To become a data scientist on Facebook, you must develop core skills like machine learning, Programming, Statistics, Mathematics, Data Manipulation and Analysis. Recruiters also explore the applicant’s interest in the company and motivation to join the Facebook community, ensuring they resonate with the mission of connecting people and building a positive impact.
How to ace the Facebook data scientist interview
14 mins read
Oct 31, 2025
Share
Content
Facebook is a dream company for many data scientists. However, many are caught off guard by the unique business-focused interview process and role for data scientists at Facebook.
Today, we’ll show you what to expect and help you practice the data science interview questions you’ll need to ace your Facebook data science interview.
Ace the data science interview the first time
Practice dozens of hands-on practice problems and brush up on your fundamentals all in one place.
Data Science at Facebook#
Data scientist is a senior role at Facebook that focuses heavily on business problems rather than engineering. Your focus will be product and market analysis to help Facebook make data-driven business decisions. This makes Facebook’s data science jobs a unique hybrid of product data analysts and data scientists.
Your day to day responsibilities will include:
- Use product and market trends to find growth opportunities, set appropriate team goals, and guide product development to meet emerging needs.
- Maintain good product sense and understand how user needs are developing.
- Act as a medium between business and engineering teams to influence new features and future marketing.
- Leverage Facebook’s huge data sets to generate proposed next moves and create approachable visuals that convey these to executives.
- Parse and weight data based on statistical significance, geographic region, bias, target populations, and potential for positive results.
These unique challenges will expose you to varied practical applications of data useful for quickly advancing your resume and career. The role also comes with one of the highest starting salaries for a non-executive position at Facebook, ranging anywhere from $120k-$190k according to Glassdoor.
Application Requirements#
Facebook is looking for candidates with a mixed background in both computer science and business analytics.
- Proficiency in Python or Java
- Experience with artificial intelligence and machine learning algorithm design
- BS/BA in Computer Science, Mathematics, or other related technical fields (or relevant bootcamp)
- 2+ years of experience in the data warehouse space
- 2+ years of experience writing SQL query statements
- 2+ years of experience with schema design
- 2+ years of experience with a OOP programming language
- 2+ years of experience with ETL design
- 2+ years of experience working with MapReduce or MPP systems
- Experience identifying deliverables, gaps, and inconsistencies in product/market data
- Experience communicating data with internal clients
Data science interview process#
The Facebook data scientist interview has 3 parts: the initial phone screening, the technical screening, and onsite interviews.
Throughout the various stages, Facebook interviewers will be testing how you think when approaching problems. Projecting your strong problem-solving skills and showing your consideration for possible obstacles is essential.
Interviewers will be listening for how your solutions and processes are optimized for scalability. Facebook collects a lot of data compared to many other tech companies, so they’re especially interested in your ability to work with big data and vast structured databases.
Finally, interviewers will want to see you approach problems from a product perspective, rather than just engineering. This means focusing on the practical application of suggestions, “what metrics can we use to gauge user involvement”, rather than the technical aspects like “how can we more efficiently store data”.
These technical aspects are still important to mention but your priority should always match the business/product focus of the role.
Initial Screening#
Set up on LinkedIn or by email, the initial screening is a 30-minute phone interview with a recruiter to discuss Facebook, your desired position/team, and clarify that you’re interested in the unique business-focused data science role that Facebook offers.
This is where you can hear more about the role and decide if it’s a good fit for you.
The technical screen#
This is another virtual screening that tests your product sense and technical skills with SQL.
Product Sense#
The product sense portion is a video interview with a current data scientist at Facebook designed to assess your ability to approach and breakdown a problem.
Finding the correct solution is not as important as effectively breaking down the problem, considering all the influencing factors, and presenting a course of action (not just data findings).
When preparing for this portion, practice:
- Deconstructing large problems its root causes and understanding the context
- Explaining your findings and suggested course of action confidently and articulately
- Presenting a hypothesis for your findings and explaining what leads you to that conclusion
- Translating data findings into actionable advice
- Engage with Facebook products as someone looking to improve the product rather than just a user
Here are some questions you can expect to encounter:
- How would you set up an experiment to evaluate a new product’s performance?
- How would you measure success for separate parts of the product, such as Facebook Live vs. client advertisements?
- How could you use current and past data to anticipate features users will want in the future?
- How would you prepare to access a new product launch?
Technical#
In this section, you’ll be given a data set and two questions to solve in code over 20 minutes (10 minutes per question).
You must solve both questions in SQL or Pandas code using a code editor called Coderpad. However, SQL is preferred as it is the standard for Facebook’s data science tools.
You’ll not be able to execute your code, so practice SQL syntax to ensure minimal syntax errors.
Along with coding each solution, you’ll have to explain the application and shortcomings of each solution. Facebook, more than other heavy data science companies like Amazon, is looking for data scientists with excellent communication skills.
For example, you may receive two tables with information about a certain Facebook Group’s activity: a usage log that contains data on each person’s time spent on the page for the last year and another table that includes the name, date of birth, occupation, and hometown of each member.
You’d then be asked to create SQL programs to find:
- How does usage differ by age in this group?
- What occupation had the highest usage difference between Friday and Saturday?
- Which factor seems to be most linked to usage?
- What percentage of users visit the group on their birthday
Keep prepping for your interview.#
Prepare for your interview right with dozens of hands-on practice problems and a comprehensive data science project. Educative’s text-based courses are made by current data scientists to let you know exactly what to expect on interview day.
Onsite interviews#
The final stage of the interview is a 2.5 hour series of interviews at either the Menlo Park, Seattle, or the New York Facebook campus. There are 4 different 30-minute interviews that each cover a different case study. There’s also a 30-40 minute lunch break to discuss the role with a current data scientist.
You have the entire 30-minute interview to answer each question:
- 1 SQL technical question
- 1 product interpretation question
- 1 quantitative analysis question
- 1 applied data question
The SQL technical question will be similar in format to the technical screening questions; you’ll receive a data set and be asked to solve problems using SQL. However, this SQL question tends to be more difficult and has a longer solution than those in the technical screening.
The product interpretation question asks you to measure product performance with details like target KPIs and how to implement a/b testing. You might be asked just to walk through this or you may have to create a high-level plan of the implementation.
An example problem for this would be:
“How would you measure the performance of X new feature?”
The quantitative analysis question is a basic statistics problem that tests if you understand the basics of statistical data analysis. Many candidates find this to be the easiest part of the interview as it is simply a baseline that you’ve not forgotten the fundamentals.
Example questions for this are things like:
- What is Bayes’ theorem and when would you use it?
- What is hypothesis testing?
- What is p-value and how do you interpret it in context?
- List assumptions about data in the context of linear regression.
- How would you explain the application of probability to your product manager?
The applied data question asks you to consider a solution at a high-level. You’ll outline your process, list any assumptions you have, describe possible shortcomings and how you’ve prepared for them, and explain how you reached your conclusions. The interviewer will ask follow-up questions during the process to see how deeply you’re thinking about this solution.
Questions for this section are intentionally broad, such as:
- Do people interact more or less on Facebook with their siblings?
- How would you measure interaction?
- How would you determine if people are siblings?
- How could Facebook use this information?
Or
- How does activity vary depending on the season? What region/s are you looking at? How would you weight activity, is a comment worth more than a like?
- What factors would you use to distinguish users?
- How could Facebook use this information?
Between two of these interviews, you’ll get a casual 30-40 minute interview with a current data scientist to ask them about their day-to-day responsibilities, challenges, and anything else you’re curious about.
This is essentially a behavioral interview to see if you’ve got the right mindset and excitement to fit the company. Ask them insightful questions that show you’re thinking about the job, and turn your charm up to 11!
Some good questions to ask are:
- What was the most difficult project of your career and how did you solve it?
- What are the unique benefits of being a Facebook data scientist?
- What tips do you wish you had when you started working at Facebook?
- What is your favorite Facebook feature to work on and why do you like it?
Meta data scientist roles: Understand the paths#
Before you dive into prep, it’s important to know that “data scientist” at Meta is not a one-size-fits-all title. There are multiple tracks, and the interview expectations differ slightly for each:
Data Scientist, Product Analytics (DS-PA): Focuses on experimentation, metrics, and product insights. Heavy emphasis on SQL, statistics, product sense, and data storytelling. This is the most common path and the one most aligned with day-to-day decision-making and product impact.
Research Scientist (RS): A more academic role focused on deep statistical modeling, causal inference, and often requiring Python or R for data analysis. Candidates are evaluated on their ability to develop methodologies and novel approaches to complex problems.
Machine Learning Data Scientist (ML DS): Sits between data science and ML engineering. The interview may include model evaluation, feature engineering, A/B testing of ML models, and deep dives into performance metrics.
Tip: Before you start preparing, clarify the exact role with your recruiter. Meta’s interview prep resources are often tailored to each track, and knowing your focus can save you weeks of unnecessary study.
Updated interview format: The modern Meta interview loop#
The interview process has evolved significantly since 2021. Most interviews today follow a 4-round virtual or hybrid loop:
Analytical Execution (45 min): Deep dive into experimentation and statistical reasoning. You might be given a product change scenario and asked how you’d design an experiment, interpret results, or address data quality issues.
Analytical Reasoning / Product Sense (45 min): Explore how you define success metrics, reason about product decisions, propose hypotheses, and measure impact. Your ability to think like a product manager is key.
SQL / Technical Interview (45 min): Solve complex, production-like data problems using SQL — often in CoderPad or another live coding environment. The interviewer may ask follow-up questions about query optimization or alternative approaches.
Behavioral (45 min): Discuss collaboration, impact, conflict resolution, and strategic decision-making. You should demonstrate both technical leadership and stakeholder influence.
Pro tip: The technical rounds are increasingly data-driven and business-oriented. Being able to connect metrics to user behavior, business KPIs, and long-term strategy is just as important as writing correct queries.
SQL expectations in 2026: Think beyond SELECT and JOIN#
SQL remains the backbone of Meta’s data science interview — but the bar is much higher than a few years ago. Instead of simple queries, you’ll encounter tasks that mimic real production challenges:
Complex aggregations and window functions: Use ROW_NUMBER(), RANK(), and cumulative sums to calculate retention and engagement.
Time-based queries: Handle event data across time zones, compute rolling averages, or create retention cohorts.
Data cleaning and transformation: Handle missing data, null values, duplicate records, and user churn scenarios.
Join optimization: Merge multiple large datasets while considering query performance.
Product metrics: Build funnels, calculate conversion rates, and compute guardrail metrics.
Example question: "Write a query to calculate the 7-day retention rate of users who engaged with a new Facebook Reels feature. How would you adjust your calculation if the feature rolled out gradually over several days?"
Experimentation and product metrics: What’s changed#
Experimentation is still the heart of Meta’s analytics culture — but the complexity and depth expected in interviews have increased. You’ll now be tested not only on how to run an A/B test but also on how to ensure its validity and interpret nuanced results.
Topics to master:
Design trade-offs: When to use A/B tests, switchback tests, or holdouts depending on product context.
Variance reduction techniques: Explain CUPED and how it can make tests more statistically powerful.
Heterogeneous treatment effects: Show awareness of segmentation and user-specific responses.
Guardrail metrics: Identify metrics to monitor for negative unintended consequences.
Metric drift and data quality: Discuss how you’d handle metrics that change meaning over time or have integrity issues.
Example prompt: "You launched a new comment ranking algorithm and saw a 5% drop in DAU but a 15% increase in session length. How would you evaluate whether the experiment was successful?"
Product sense: Think like a PM#
Product sense interviews are designed to test how you think about product growth and impact. The most successful candidates show they can balance data rigor with strategic thinking.
You should be able to:
Define success metrics aligned with long-term company goals.
Develop hypotheses to explain metric changes and propose solutions.
Prioritize metrics based on user experience, growth, and business value.
Weigh trade-offs between short-term engagement and long-term retention.
Communicate insights clearly to non-technical stakeholders.
Example prompt: "Facebook Marketplace revenue has plateaued over the last quarter. How would you investigate the root cause and propose next steps?"
Behavioral interviews: Storytelling matters more than ever#
Behavioral interviews at Meta now focus heavily on impact, influence, and leadership. They assess how you work cross-functionally and how data has shaped product decisions in your past roles.
Prepare 4–5 stories highlighting:
Influence: Times you used data to shape strategy or product direction.
Collaboration: Examples of successful partnerships with PMs, engineers, or executives.
Conflict resolution: Situations where you resolved disagreements or pushed back using data.
Decision-making: Cases where you balanced imperfect data with business priorities.
Learning from failure: Instances where an analysis didn’t go as expected and what you learned.
Pro tip: Structure your answers with the STAR method and focus on quantifiable outcomes — metrics moved, features shipped, or user behaviors changed.
Ethics and data privacy: The “bonus” topic that’s becoming standard#
With increasing scrutiny over user data, privacy, and fairness, Meta interviewers are now more likely to probe your awareness of ethical data practices.
Be ready to discuss:
How you ensure experiments comply with data privacy regulations (GDPR, CCPA, etc.).
Ways to identify and mitigate bias in data and algorithms.
Strategies for monitoring unintended consequences of product launches.
Approaches to designing fair and inclusive metrics.
Example prompt: "You’re tasked with designing a recommendation system for Instagram Explore. How would you ensure it doesn’t amplify harmful or biased content?"
How to prepare for your interview#
As you approach your interview, remember:
- Failing one part of the interview does not mean you won’t get the job, keep your confidence up.
- Recruiters are evaluating your attitude and friendliness as much as your skills, don’t forget your behavioral interview skills.
- Brush up on your machine learning, SQL query fundamentals, and statistics fundamentals to make sure you don’t make any avoidable mistakes.
The best way you can prepare for this interview is with hands-on practice.
To let you brush up on your fundamentals and data science skills right, Educative has created the course Grokking Data Science. This course includes over 10 hours of learning material from statistics fundamentals, to advanced machine learning. In the end, you’ll cement your learning with experience by completing a real-world machine learning project.
With this course, you’ll be able to walk into your Facebook interview with confidence!
Happy learning!
Continue reading about data science interviews#
Frequently Asked Questions
How do I become a data scientist on Facebook?
How do I become a data scientist on Facebook?
Written By:
Ryan Thelin
