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:
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:
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.
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.
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.
The video-chat will last roughly 45 minutes, broken down as follows:
At a high-level, your interviewer will be looking for:
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.
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:
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.
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:
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.
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.
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.
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:
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:
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.
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.
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:
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:
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.
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!
This portion of your interview (only covered onsite) will test your quantitative reasoning and applied stats knowledge.
Sample Quantitative Analysis Questions:
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.
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: