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Facebook Data Scientist Interview

Read our guide on what to expect in the Facebook interview and how to prepare.

Want to become a data scientist at Facebook or Meta? You’re not alone. It’s one of the most sought-after roles in tech, and requires top-notch analytical and communication skills.

Meta is the first to admit that the data science role at Facebook is defined a little differently than at other companies.

Data scientists at Facebook often work more closely with the business team than the engineering team.

Your core responsibilities as a data scientist at Facebook will be:

  • To use quantitative tools to uncover opportunities, help clarify goals, and give input to guide the product roadmap.
  • To explore, analyze, and aggregate large data sets to draw conclusions and communicate them cleanly.
  • To design informative experiments.
  • To collaborate with engineers on logging, product health monitoring, and experiment design/analysis.

What does a Meta or Facebook data scientist do?

Facebook data scientists use their technical skills and analytical aptitude to help support the growth of Facebook products.

Data scientists at Facebook perform many of the same duties as those at other companies.

However, Facebook differs from other major tech companies in that its Data Science role is much broader.

Data scientists at Facebook are heavily involved in establishing team goals, discovering new opportunities, and making business-related predictions They do this all while serving as the standard-bearers for the experimental corporate culture Facebook wishes to foster.

Data scientists at Facebook use their skills and knowledge to help support and grow the company's products. They look for ways to interpret data that can create more value.

Here's what a typical data science job description at Facebook could look like:

  • Leverage data and business principles to drive large-scale Facebook’s Data Center programs.
  • Define and develop the program for metrics creation, data collection, modeling, and reporting the operational performance of Facebook’s data centers.
  • Collaborate with cross-functional data and business teams to define problem statements, access and manipulate data, build analytical models, explain data-gathering requirements, deliver analytics insights, and make recommendations.
  • Define, compute, track, and continuously validate business metrics with descriptive and predictive analytics. Leverage tools like Python, SQL, R, and Tableau for analytics.
  • Design & Implement statistical models such as Hypothesis testing, Forecasting, statistical process control, and simulation to evaluate critical business decisions and influence our Data Center planning & Operations.
  • Identify gaps in the operational processes, build analytical models for finding insights, and help in driving decisions across different org leadership.
  • Provide mentorship to other members of the team on the development of best practices for the design and implementation of cutting-edge analytics insights.
  • Lead and support various ad hoc projects, as needed, in support of Data Center strategy.

What are the typical job requirements for a Facebook Data Scientist?

  • A minimum of 2 years of work experience in analytics (minimum of 1 year with a Ph.D.)
  • Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R)
  • Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed before joining Meta.

Recommendations before you begin applying for Facebook data science roles:

  • Turn your resume into a story: communicating complicated information simply is paramount at Facebook. Since you’ll be making complex data interpretable to lots of non-scientists, you’ll want to make sure your abilities in this area shine. Your resume is the perfect way to demonstrate this from the very beginning. Make sure you can tell a coherent, compelling story around all the experiences listed on your resume, and why you’re an ideal candidate. Prepare your “elevator pitch” of varying lengths (30 seconds, 2 minutes, 5 minutes) and perfect it. Be prepared for follow-up questions.
  • Mock interviews: Exponent's coaching services are your best friend. Try mock interviews until you feel comfortable with the topics below. Don’t just work with other engineers and data scientists — grab a non-tech friend and explain your resume-building exploratory data analysis. You want to make sure you can explain yourself to non-engineers.
  • Reach out: Don’t be afraid to reach out. Find a few Facebook data scientists within the Exponent community and ask them about their experiences. They’ve gone through what you’re going through now, and they’re great sources of information and support.

Interview Process

Facebook Data Scientist Interview Stages

Like many interviews at Big Tech companies, the Facebook interview process will happen in several stages. Before Meta invites you to an onsite interview, you will first need to complete some preliminary screenings:

You’ll have an initial video conference with a data scientist followed by an in-depth onsite interview. Let’s break these down.

Recruiter Phone Screen

Most candidates will begin their interviews by speaking with a recruiter on the phone. In many cases, this will be a member of HR at Facebook.

Although this screening may be general and non-technical in nature, you still need to take it seriously to continue to the other more consequential stages.

The recruiters generally ask candidates about their resumes and previous experience, along with some basic behavioral questions. When it comes to DS candidates, these questions may be focused on SQL skills and product strategy (chances are, though, these may be limited to the subsequent technical screen).

So long as you pass this initial recruiter phone call, they will schedule the next round soon after.

Technical Phone Screen

In most cases, DS candidates at Facebook will only need to complete one technical screening to move on to the onsite interviews. This, of course, means that candidates need to ace this preliminary round if they want to go any further.

Luckily, these technical screenings also provide a bit of preparation for the onsite interviews, considering the questions asked will be very similar to those during these onsite interviews.

It's virtually guaranteed that you will experience interview questions regarding SQL, quantitative analysis, and product sense (we'll go into more detail shortly).

Feeling nervous and want some extra prep for your data science interview beforehand? Check out our question database.

Facebook Data Science Technical Phone Screen

The video-chat will last roughly 45 minutes, broken down as follows:

  • Analytical (10-20 minutes)
  • Technical (10-20 minutes)
  • Q&A (5 minutes)

At a high-level, your interviewer will be looking for:

  • Framing: How you deal with ambiguity. Can you see the underlying structure behind data? Can you distill an open-ended question?
  • Operationalization: Can you draw actionable insight from data?
  • Analytical Understanding: Your ability to articulate your conclusions. Can you translate between numbers and words?
  • Hypothesis Driven: Can you identify (and logically support) reasonable hypotheses? Can you look at data, make a decision to support/refute a product insight, and explain yourself?

As with most analytical/technical interviews, your interviewer is looking for insight into your thought process and problem-solving approach. Your creativity and ability to articulate complex topics simply should be front and center; this is more important than arriving at the “correct” answer.

Product Sense and Analytics in the Data Science Pre-Screen Interview

Most data scientists at Facebook work under the umbrella of Product Analytics. Of course, many others work in different departments, such as Virtual Reality, Machine Learning, or Remote Presence. However, new data scientists at the company will likely start in Product Analytics.

You'll get asked plenty of product sense and analytical questions during these technical screenings and during onsite interviews.

Product sense questions are designed to evaluate a candidate's ability to solve business questions and problems (in this case, using quantitative data).

Product sense questions are typically pretty vague or abstract. This is by design because it allows the interviewer to see how you solve the problem at hand. In many cases, these questions are focused on business problems related to Facebook products. They are not interested in a candidate finding the "right" answer.

To help prepare for these questions, be sure to ask yourself the following:

  • Why did the developers make the product decisions that they did?
  • How are some ways that the product could be improved?
  • When considering the success, growth, and effectiveness of a product, what are the most relevant metrics?
  • What defines success for a particular product and why?
  • What are the relevant analytical metrics that should be used when considering business problems associated with a particular product?
  • What are the ways that new product improvements could be implemented and analyzed?
  • We have plenty of Product Design and Product Strategy interview questions you can review to help you prepare for this part of the Facebook DS interview.

Technical Questions in the Data Science Pre-Screen Interview

As the name suggests, the technical screening will undoubtedly involve technical interview questions.

Even the product analytic questions will likely be technical in nature.

After answering some product/analytical questions, the technical recruiter will likely give you two technical interview questions. Facebook certainly requires its DS candidates to have superb coding skills, but that's not all.

Your recruiter will evaluate if you can take a high-level technical problem and transform it into an execution strategy and actionable solution.

You must also explain your decision-making and why this particular technical solution is pertinent to the problem, along with its shortcomings or tradeoffs.

Generally speaking, these technical questions in a DS interview will involve a dataset with a request to write a SQL query or Pandas code to achieve the desired result.

Keep in mind your interviewer will likely not give you any genuine data during these problems, so you must practice and become familiar with SQL syntax.

Onsite Interview

After passing both the initial recruiter screening and the technical screening, candidates will be invited to a series of onsite interviews.

For aspiring data scientists, hiring managers at Facebook conduct four separate interviews. Each round lasts 30 minutes.

These onsite interviews will consist of:

  • Analysis Case: Product Interpretation
  • Analysis Case: Applied Data
  • Quantitative Analysis
  • Technical Analysis

Expect to go deeper into what you covered during your video conference. Your storytelling abilities will be front and center here as well (this thread runs through the entire facebook interview process).

As a data scientist, you’ll be expected to make data-driven decisions and give valuable input into product decisions so you should be prepared for product sense questions as well as analytical/technical.

Facebook’s onsite case discussions will be centered around Facebook products, so educate yourself on what constitutes a "Facebook Product". The topics featured aren’t exhaustive, so be prepared to go off-book.

Analysis Case Study: Product Interpretation

Data science candidates will be asked to complete a product case study. This product case study involves using data regarding user behavior to discover new ideas or insights for the future.

Hiring managers at Facebook ask this interview question to give data science candidates the chance to demonstrate their ability to glean product insight from user data.

Considering these Product Interpretation questions are usually abstract or open-ended, the best way to start is to ask clarifying questions. This will help you better understand the parameters at hand and provide the best possible answer you can.

Goal: Showcase your ability to translate user behavior into product insights.

You’ll be given a fairly open-ended hypothetical or case study question. A good first step is always to clarify scope (in fact, your interviewers are looking for you to do this).

Ask a few questions to help you understand parameters. From there:

  • Hypothesize first: Lay out a few reasonable hypotheses on how to improve a product given the scope of your question and any constraints you’ve ascertained. Present a few initial ideas on ways to improve the product according to the constraints and parameters of the interview question,

  • **Explain your thinking: **Make sure you can back these up logically. Consider tradeoffs and outline your choice in terms of metrics. Always explain your thinking and decision-making. Include details regarding tradeoffs in your answer and illustrate the rationale behind your decision using relevant metrics

  • Design and implement : Flesh out the design and implement your potential solution.

  • Interpret the results and communicate them cleanly. Analyze the data and share it with your hiring manager clearly and succinctly.

Analysis Case Study: Applied Data

This interview round is getting closer to the bread and butter of the data scientist role at Facebook. This interview stage will be a bit more technical than the product interpretation round.

Nevertheless, you will still be expected to solve product problems using insights from various datasets.

To ace the Applied Data round, you will need to demonstrate to your hiring manager that you are capable of solving problems using data from beginning to end.

Beware, however, that these are the interview rounds that many candidates can get lost in. Remember that each of these meetings is only 30 minutes long.

The best thing you can do to prepare for this stage is by studying how Facebook, as an organization, uses data science writ large. Explore Facebook's many products from the perspective of a data analyst rather than a typical user.

Ask yourself questions such as:

  • What technical frameworks are used in what products?
  • How can success be measured, and what metrics are used to measure it?
  • How can new features or experiments be tested? How can their success be measured?

Goal: show how you go about solving problems with data, from start to finish.

It’s easy to get lost on questions this open-ended.

Before you even begin to prepare, spend some time learning what data science means to Facebook and Meta. Play with Facebook products as a scientist rather than a user - how do you think they evolved as they did?

What technical framework underlies each? What metrics would you use to measure success? And how would you test/implement new features?

Once you’ve got an understanding of how Facebook operates:

  • Consider available data sets. Which will be most helpful in solving your problem? Why?
  • Draw inferences and explain. Summarize multiple insights into a meaningful, data-informed conclusion.
  • Map insights back to product impact. If you have multiple suggestions, which would you prioritize? - Remember to always be user-focused.

Quantitative Analysis

Like the previously listed Applied Data stage, it should be no surprise that another section of the Facebook interview is dedicated to Quantitative Analysis.

Here, candidates will demonstrate their quantitative reasoning and understanding of various mathematical, statistical, and probabilistic concepts in data science. Not only that but they will be asked some applied statistics questions to assess if they can use these analytical skills outside the interview.

To ace the Quantitative Analysis round, candidates will need to demonstrate proficiency with fundamental statistical skills and how they can be applied to solving product-related problems.

Goal: Show competency with basic stats and how they apply to product decisions.

Time to brush up on basic math, statistics, and probability, and how they apply to Facebook products. You won’t need to know advanced machine learning models or linear algebra, but you should be familiar with descriptive statistics, common distributions (binomial or normal), the central limit theorem, the basics of linear regression, and Bayes theorem.

Technical Analysis

Goal: Answer open-ended product questions with careful data analysis/manipulation with code.

Facebook coding interviews are all whiteboarding with no pseudocode allowed. You can choose whichever language you’re most comfortable with (though most questions are designed with SQL in mind) and little mistakes in grammar/syntax are fine, but you’ll need to explain yourself fully -- no glossing over details.

Sample Interview Questions


Analytical questions will show up in your initial interview and in two case studies during your onsite (product interpretation and applied data). Your job is to show that you know your way around data, and that you can draw reasonable conclusions from that data which will impact product decisions in a way which aligns with Facebook’s vision.

Product sense will also be tested here. Rather than diving straight into metrics, start with the goals of the product and expand logically. If you struggle to get started, try going "going broad then deep". If you have too many ideas and can’t choose one, give The Triangle Method a try. We recommend connecting with product managers in the Exponent community for some mocks if you want to hone your product sense.

Sample Analytical Questions:

  • How would you evaluate YouTube’s video recommendations?
  • How do you frame a problem, from selecting the most suitable data sets all the way through execution?

For additional review, check out:

Practice product design, product strategy, and analytical interview questions in our interview question database.


You’ll get data-processing questions in your initial interview, and you’ll do some coding in your onsite interview. The environment will be whiteboard or plain text only, so don’t rely on help documentation or autocomplete. Facebook expects some grammar/syntax errors, but no pseudocode. You’re free to use whatever language you like, but most questions were designed to be answered using SQL, Python, or R.

You should be comfortable:

  • Grouping and using aggregate functions.
  • Utilizing different types of joins (left, inner, outer, etc.) including when and how to use a self-join.
  • Appending multiple data sources (union in SQL, concat in Pandas, bind_rows in R).
  • Filtering data by multiple, complex conditions.• De-duplicate, sort, handle missing / incomplete data.
  • Assessing efficiency. This won’t be a primary focus, but your interviewers may ask you to think of more efficient ideas or to explain why you’re making certain efficiency / simplicity tradeoffs.

Sample Technical Questions:

You're given a school attendance log with student_id and attendance, and a summary table with student_id, school_id, grade_level, date_of_birth, hometown.

  • What percentage of students attend school on their birthday?
  • Which grade level had the largest drop in attendance between yesterday and today?

Facebook recommends practicing as much as possible in a similar environment — and they’ve provided some resources to guide you:

It might also help to review Exponent’s bank of commonly asked technical or data structures problems. They’re geared for Software Engineers, but hey — if you’ve got the time, it’s always better to be prepared!

Quantitative Analysis

This portion of your interview (only covered onsite) will test your quantitative reasoning and applied stats knowledge.

Sample Quantitative Analysis Questions:

  • What do you think the distribution of time spent per day on Facebook looks like? What metrics would you use to describe that distribution?
  • How do you apply A / B testing?
  • Do you understand common distributions?

For any data science interview, you’ll want to have a strong grasp of basic stats, mathematics, and probability as it applies to your target company. But at Facebook especially, remember to think like a product owner. No, this doesn’t mean stepping into a product manager’s shoes. Remember that your data skills will always affect product decisions, and product context should infuse every step of your analysis. While preparing for quantitative analysis questions, read through Facebook’s about page and make sure that your recommendations propel the company forward in the right direction.

You can review statistics in many different ways, but Facebook recommends the combinatorics page on brilliant.org -- don’t get sucked into all the fun math questions! Eyes ahead; cover your bases and move on.

Tips and Strategies

  • Think out loud. Your answers, while important, are less important than your thought process. Your interviewers want to get a sense of how you think. Providing a logical narrative of your thought process as you answer questions will showcase your problem-solving abilities as well as communication skills. Slam dunk.
  • Deconstruct problems. Break down complicated, ambiguous problems into groups. Recombine groups in a way that makes sense, and we’re confident a solution will become clear.
  • Lookout for hints. Listen carefully to interviewer prompts; be ready to pivot if you sense that you’re being redirected.
  • Clarify. Ask clarifying questions. Don’t assume.
  • Don’t forget to say why you want to work at Facebook. While the Facebook interview doesn’t explicitly include behavioral questions, they’re looking for people who know the environment and all the challenges that go with it. They’re also looking for someone collaborative and fun. Don’t be afraid to show a little personality.
  • Ask questions. Don’t get so caught up in the technical/analytical that you forget to ask about the job, the company, and the culture.

Some Additional Resources:

It’s important to have an understanding of the company going into the interview, and Facebook has a complicated set of products. Check out the below to better understand Facebook’s unique structure and mission: