Facebook data scientist interview: the only post you'll need to read

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Data scientist interviews at Facebook are really challenging. The questions are difficult, specific to Facebook, and cover a wide range of topics.

The good news is that the right preparation can help you maximize your chances of landing a job offer, and we've put together the ultimate guide below to help you succeed.

Here's an overview of what we'll cover:

1. Interview process and timeline

1.1 What interviews to expect

What's the Facebook data scientist interview process and timeline? It typically takes four to eight weeks and follows these steps:

  1. Application and referrals
  2. Recruiter call (~15 min). 
  3. Tech interviews (1-2 interviews, 45min each). 
  4. Onsite interviews (4 interviews, 30-60min each).

    Let's look at each of these steps in more detail below:

    1.1.1 Application and referrals

    Step one is getting a Facebook interview in the first place. In this guide we're focusing primarily on the interviews, so we'll keep this portion brief. You can apply to Facebook directly or a recruiter may reach out to you via LinkedIn (or similar). In either case, it helps to have a quality (and up-to-date) resume that is tailored to data scientist positions, and to Facebook more specifically. 

    It can also be helpful to get an employee referral to the Facebook recruiting team internally. This may not be possible, but if you do have a connection to someone who works at Facebook, then this can help you get your foot in the door for an interview.

    1.1.2 Recruiter phone screen

    In most cases, you'll start your interview process with Facebook by talking to an HR recruiter on the phone. But don't underestimate this initial interview. Although for many roles the initial phone screen is used to ask basic resume and behavioral interview questions, for Facebook data scientists it often includes SQL, product, or metric questions. So make sure you're ready from the beginning.

    If you get past this first HR screen, the recruiter will then help you schedule the next round. One great thing about Facebook is that they are very transparent about their recruiting process. And once you've been invited to the next round, they will likely give you some additional information about what to expect in their interview process.

    1.1.3 Technical interviews

    Next, you'll go through one or two 45min technical interview(s). The typical process is to just have one technical screen and then to advance to the onsite interviews. However, in some cases, candidates will have two technical screens before receiving their offer decision (i.e. there would be no onsite interviews in this case). If you're not sure which process applies to your role and location, then just wait and you should find out after you pass the recruiter phone screen.

    The types of questions you'll be asked during the technical interview(s) are similar to the questions you'll encounter during the onsite interviews (see below). In particular, be prepared to answer SQL and product questions. To prepare for these, see the preparation tips in section 3 below and also get some practice with the questions in section 2. 

    1.1.4 Onsite interviews

    The final stage in the interview process for Facebook's data scientist candidates, is the onsite interviews. The onsite typically includes 4 interviews, and will take-up a half-day of your time. 

    During your onsite interviews, you're going to need to be prepared for a variety of questions. In particular, there are four main types of questions that you'll encounter during your onsite interviews. Here's a quick summary: 

    1. Programming questions, where you'll be tested on your data analysis skills through SQL, modeling, or data structure and algorithm questions.
    2. Product / business sense questions, where you'll need to demonstrate your ability to provide useful insights and to drive product and business decisions.
    3. Statistics questions, where you'll be asked about statistics concepts and your experience applying them in your past projects. 
    4. Behavioral questions, where you'll be asked about your current or past projects, and your motivation for applying to the role at Facebook. 

    It's also worth mentioning, that the questions you're asked in the onsite interviews tend to be more difficult than the questions from the technical round. So, don't let yourself be too confident after the early interviews, and double down on your preparation for the onsite interviews!

    [COVID note] It's likely that your onsite interviews will be held virtually instead of in-person, given the COVID-19 pandemic. However, your recruiter should be able to provide you the most up-to-date information on Facebook's onsite interview procedures. Feel free to ask your Facebook recruiter for details, just wait until you've been officially invited to participate in the onsite interviews.

    1.2 What happens behind the scenes

    Throughout the interview process at Facebook, the recruiter usually plays the role of "facilitator" and moves the process from one stage to the next. Here's an overview of what typically happens behind the scenes:

    • After the technical interview, the interviewer(s) you've talked to submit their ratings and notes to the internal system. Your recruiter then reviews the feedback, and decides to move you to the onsite interviews or not depending on how well you've done.
    • After the onsite, the interviewers will make a recommendation on hiring you or not and the recruiter compiles your "packet" (interview feedback, resume, referrals, etc.). If they think you can get the job, they will present your case at the next candidate review meeting.
    • Candidate review meetings are used to assess all candidates who have recently finished their interview loops and are close to getting an offer. Your packet will be analyzed and possible concerns will be discussed. Your interviewers are invited to join your candidate review meeting, but will usually only attend if there's a strong disagreement in the grades you received (e.g. 2 no hires, 3 hires).  At the end of the candidate review meeting, a hire / no hire recommendation is made for consideration by the hiring committee.
    • The hiring committee includes senior leaders from across Facebook. This step is usually a formality and the committee follows the recommendation of the candidate review meeting. The main focus is on fine-tuning the exact level and therefore the compensation you will be offered.

    It's also important to note that hiring managers and people who refer you have little influence on the overall process. They can help you get an interview at the beginning, but that's about it.

    2. Example questions

    Let's dive into the four primary types of interview questions you can expect during your Facebook data scientist interviews:

    • Programming
    • Product / business sense
    • Statistics
    • Behavioral

    In the below subsections, we've put together a high-level overview of each type of question. In addition, we've compiled a selection of real Facebook data scientist interview questions, according to data from Glassdoor. These are great example questions that you can use to start practicing for your interviews. If you're looking for even more practice questions, check out our general data science interview prep guide.

    2.1 Programming questions

    Facebook data scientists work with the company's vast datasets to understand and solve real-world problems. And the data analysis and data manipulation that a Facebook data scientist does day-to-day requires technical knowledge. 

    As a result, your Facebook interviewers will ask questions that test your technical skills. This includes a few different types of programming, but the most common technical questions are about SQL. So, you should be well prepared to write SQL queries (with proper syntax) during your interviews.

    In addition to SQL questions, you should also be ready for questions related to modeling, data structures, and algorithms. Although these questions are less frequently asked, you want to make it clear to your interviewers that you have the breadth and depth of technical knowledge required to succeed in the role.

    Below, we've listed several example questions that were asked in Facebook data scientist interviews, according to data from Glassdoor. Note that we've edited the language in some places to improve the clarity or grammar.

    Facebook data scientist interview questions - Programming

    • SQL
      • Provided a table with user_id and the dates they visited the platform, find the top 100 users with the longest continuous streak of visiting the platform as of yesterday.
      • Provided a table with page_id, event timestamp, and an on/off status flag, find the number of pages that are currently on.
      • Given a database of posts and a database of comments on those posts, how do you determine how many conversations are happening in the comments per post on average?
      • You're given two tables. One contains the date, post_id, relationship (e.g. friend, group, page), and interaction (e.g. like, share, etc.). The second table contains post_id, and the ID of the person who posted. How many likes were made on friend posts yesterday?
      • What's the difference between a left join, a union, and a right join?
    • Data structure and algorithms
      • There is an algorithm that rates posts on their likelihood to be spam. how would you check if the algorithm works?
      • Given a list, search for consecutive numbers (n) whose sum is equal to a specific number (x).
      • Given a list of people with things that they own, find the people who have common items and what they are.
      • Can you find the first date of log on for a platform, given a list of users?
      • How do you revert a string?
    • Modeling
      • How would you create a model to find bad sellers on marketplace?
      • How can you tell if your model is working?
      • How can you predict Samsung phone sales?
      • How can you predict churn rate? 

    2.2 Product / business sense questions

    At the end of the day, Facebook's data scientists help to drive product and business decisions. Data scientists use a variety of techniques to generate insights, but those insights are ultimately used to improve Facebook's products and grow the business.

    With that in mind, you should be ready to answer questions that are designed to assess your product and business sense. For example, Facebook tends to ask questions about metrics, including how to set good metrics, and how to react to metric changes. In addition, you should be prepared for questions about Facebook's products and how they could be improved.

    Below, we've listed several example questions that were asked in Facebook data scientist interviews, according to data from Glassdoor. Note that we've edited the language in some places to improve the clarity or grammar.

    Facebook data scientist interview questions - Product / business sense

    • Metric
      • What would you consider to be a metric for meaningful social interactions for posts on the news feed?
      • What metrics would you track to measure a product's success?
      • Facebook user groups have gone down by 20%, what will you do?
      • How would you investigate a 10% drop of the friends application?
      • What would be a good click rate to aim for?
    • Product
      • How would you improve notifications?
      • What's your favorite Facebook product and how can we improve it?
      • What functionalities would be helpful in the creation of reactions on Facebook?
      • How would you setup an experiment to understand a feature change in Instagram stories?
      • Facebook wants to design a local refer page, how would you use data to help accomplish this goal?
    • Other
      • How would you determine if X is a good idea?
      • We have X product and Y goal, how would you address this problem?
      • How many orders of fries does McDonald's sell in a year?

    2.3 Statistics questions

    Facebook data scientists have to derive useful insights from large, and potentially complex, datasets. In order to do this, it pays to have a strong understanding of statistics. 

    Prior to your interviews you should take some time to brush up on statistics fundamentals and to practice giving concise explanations of statistical terms (e.g. p-value, recall, etc.). In addition, it's pretty common to get questions related to A/B testing, so if you have experience using A/B tests, we'd recommend preparing a specific example in advance.

    Below, we've listed several example questions that were asked in Facebook data scientist interviews, according to data from Glassdoor. Note that we've edited the language in some places to improve the clarity or grammar.

    Facebook data scientist interview questions - Statistics

    • How would you do an A/B test on a new metric to see if it truly captures meaningful social interactions?
    • Have you ever done A/B testing?
    • What is R squared? Can it take negative values?
    • How would you explain p-value to non-technician?
    • What is "recall", can you explain it from scratch?

    2.4 Behavioral questions

    In addition to the question types highlighted above, you can expect to be asked behavioral or "resume" questions about your past work experience and your motivation for applying to Facebook. Indirectly, these questions also evaluate your communication skills. 

    Behavioral questions are a great opportunity to tell your story (in a concise way), and to demonstrate your alignment with Facebook's values and culture. If you're applying directly to a job posting, you can also be strategic by aligning your answers for behavioral questions with the top qualifications that are listed in the job description. 

    Below, we've listed several example questions that were asked in Facebook data scientist interviews, according to data from Glassdoor. Note that we've edited the language in some places to improve the clarity or grammar.

    Facebook data scientist interview questions - Behavioral

    • Why Facebook?
    • Why data science?
    • Tell me about yourself
    • Tell me about your current project
    • What's the biggest challenge you've faced?
    • What's your weakness? 
    • Tell me about your interests

    3. How to prepare

    Now that you know what questions to expect, let's focus on how to prepare. Here are the four preparation steps we recommend to help you get an offer as a Facebook data scientist.

    3.1 Learn about Facebook's culture

    Most candidates fail to do this. But before investing a ton of time preparing for an interview at Facebook, you should make sure it's actually the right company for you.

    Facebook is prestigious and so it's tempting to assume that you should apply, without considering things more carefully. But, it's important to remember that the prestige of a job (by itself) won't make you happy in your day-to-day work. It's the type of work and the people you work with that will.

    If you know data scientists, engineers, or PMs who work at Facebook (or used to) it's a good idea to talk to them to understand what the culture is like. In addition, we would recommend reading about Facebook's 5 core values and hacker culture.

    3.2 Practice by yourself

    As mentioned above, you'll encounter four main types of interview questions at Facebook: programming, product / business sense, statistics, and behavioral.

    For the programming interview questions, your biggest priority should be to practice with example questions. You should practice with every type of programming question covered in section 2.1. However, you should pay special attention to SQL questions. To help with that, we'd recommend reading this analysis of the 3 "types" of SQL problems.

    For the product / business sense questions, you're dealing with problems that are similar to what product managers at Facebook would work on. As a result, we'd recommend studying our product management guides on metric, favorite product, product improvement, and estimation questions. These guides will equip you with a method for answering the majority of the product / business sense questions you're likely to encounter as a data scientist candidate.

    For statistics questions, we'd recommend brushing up on statistics fundamentals. For people who are new to data science, or just need a refresher, this article on freeCodeCamp provides a good high-level starting point. In addition, if there are specific statistics topics that you want to brush up on, like p-value or regression, then consider watching the corresponding Crash Course videos in this playlist on YouTube

    For behavioral interview questions, we recommend learning our step-by-step method for answering behavioral questions. You can then use that method to practice answering the example behavioral questions provided in section 2.4 above.

    Finally, a great way to practice all of these different types of questions is to interview yourself out loud. This may sound strange, but it will significantly improve the way you communicate your answers during an interview. Play the role of both the candidate and the interviewer, asking questions and answering them, just like two people would in an interview. Trust us, it works.

    3.3 Practice with peers

    Practicing by yourself will only take you so far. One of the main challenges of data scientist interviews at Facebook, is communicating your different answers in a way that's easy to understand.

    As a result, we strongly recommend practicing with a peer interviewing you. If possible, a great place to start is to practice with friends. This can be especially helpful if your friend has experience with data scientist interviews, or is at least familiar with the process. You can also find peers to practice with on our mock interview platform.

    3.4 Practice with ex-interviewers

    Practicing with peers can be a great help, and it's usually free. But at some point, you'll start noticing that the feedback you are getting from peers isn't helping you that much anymore. Once you reach that stage, we recommend practicing with ex-interviewers from top tech companies.

    If you know a data scientist or someone who has experience running interviews at Facebook or another big tech company, then that's fantastic. But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

    This problem is exactly why we're preparing to launch a coaching service for data scientists, where you can get one-on-one practice and feedback from Facebook ex-interviewers. Drop-in your email address below to get notified when we launch!

    Any questions about Facebook data scientist interviews?

    If you have any questions about Facebook data scientist interviews, do not hesitate to ask them in the comments below. All questions are good questions, so go ahead!