Data scientist interviews at Meta (formerly Facebook) are really challenging. The questions are difficult, specific to Meta, 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:
Click here to practice 1-on-1 with data science ex-interviewers
1. Interview process and timeline
1.1 What interviews to expect
What's the Meta data scientist interview process and timeline? It typically takes four to eight weeks and follows these steps:
- Resume screen
- Recruiter call (~15 min).
- Tech interviews (1-2 interviews, 45min each).
- Onsite interviews (4 interviews, 30min each).
Let's look at each of these steps in more detail below:
1.1.1 Resume screen
First, recruiters will look at your resume and assess if your experience matches the open position. This is the most competitive step in the process, as millions of candidates do not make it past this stage.
If you’re looking for expert feedback on your resume, you can get input from our team of ex-Facebook/Meta recruiters, who will cover what achievements to focus on (or ignore), how to fine tune your bullet points, and more.
It can also be helpful to get an employee referral to the Meta recruiting team internally. This may not be possible, but if you do have a connection to someone who works at Meta, 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 Meta 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 Meta data scientists it often includes SQL and product analysis 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 Meta 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 screen
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 analysis case questions. To prepare for these, get some practice with the question examples in section 2 below and also see the preparation tips in section 3.
1.1.4 Onsite interviews
The final stage in the interview process for Meta's data scientist candidates, is the onsite interviews. As outlined by Meta's very useful onsite prep guide, the onsite typically includes 4 interviews of 30 minutes, consisting of:
- Analysis case: product interpretation. This is a product case study which focuses on how you translate user behaviour into ideas and insights using data and metrics.
- Analysis case: applied data. Here you'll get a bit more technical, focusing on how to solve a product-related problem using data.
- Quantitative analysis, where you'll face quantitative reasoning questions that test your knowledge of relevant mathematical, statistical and probabilistic concepts, along with applied statistics questions that will see if you can apply these concepts to real-world problems.
- Technical analysis. This is a coding interview where you'll analyze an open-ended product problem and try to solve it through code.
You'll need to work through your solutions on a whiteboard, or the online equivalent if you're not there in person. 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, be sure to double down on your preparation for them!
[COVID note] It's likely that your onsite interviews will be held virtually instead of in-person, since the COVID-19 pandemic. However, your recruiter should be able to provide you the most up-to-date information on Meta's onsite interview procedures. Feel free to ask your Meta 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 Meta, 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 Meta. 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
Now we've covered the process, let's get into the type of questions you can expect for each type of interview. Bear in mind that the technical screen draws from the same questions types as the onsite interviews, just in less depth.
Note that many of the questions below are asked in the form of case studies. For more information about data science case study interviews, take a look here.
In the below sub-sections, we've also compiled a selection of real Meta data scientist interview questions, according to data from Glassdoor. These are great example questions that you can use to start practicing for your interviews.
2.1 Analysis case: product interpretation questions
At the end of the day, Meta's data scientists help to drive product and business decisions. They need to be able to understand problems from a range of perspectives, stepping back from the data to consider how features and metrics will affect the user experience.
With that in mind, the first of the two analysis case interviews - product interpretation- will ask you to think a bit more like a product owner.
Meta describes this interview as "a product case study focused on interpretation of user behavior using data and metrics." You'll be asked to analyze Meta products, discuss how they could be improved and explain how you would measure this improvement, or react to metric changes.
Let's take a look at some questions.
- What's your favorite Meta product and how we can improve it?
- What Meta products are you familiar with?
- How would you measure the success of a product?
- What KPIs would you use to measure the success of the newsfeed?
- Which functionalities would be helpful in the creation of reactions on Facebook?
- How would you improve notifications?
- Activity in Facebook user groups is down by 20%, what do you do?
- Friends acceptance rate decreases 15% after a new notifications system is launched - how would you investigate?
2.2 Analysis case: applied data questions
For the second case study interview - applied data - you'll be given a more specific product-related problem and asked to come up with an approach to solving it using a dataset that the interviewer will provide.
On top of the data analysis you'll need to consider how to frame the problem, what metrics to use, a/b testing, technical trade-offs, etc.
Let's take a look at some example questions.
- How would you use data to confirm that users’ high school data is real? (solution)
- How would you evaluate the impact for teenagers when their parents join Facebook?
- How would you decide to launch or not if engagement within a specific cohort decreased while all the rest increased?
- How would you set up an experiment to understand feature change in Instagram stories?
- How would you predict churn rate?
- Given a table of data, how would you create a model to detect spam?
- How would you create a model to find bad sellers on marketplace? How can you tell if your model is working?
2.3 Quantitative analysis questions
Data scientists at Meta need to have a deep knowledge of statistics and be able to apply it to business problems.
With this in mind, quantitative reasoning questions will test you on mathematical, statistical and probabilistic concepts and your ability to use them in a business scenario.
Then the applied statistics section focuses more on using stats to solve real-world data problems. 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.
It's worth noting that Meta says you won't face any specific machine learning questions but if you have the relevant knowledge, you can weave it into your answer to deepen the discussion.
Let's take a look at some questions.
Quantitative reasoning
- How do you explain p-value to non-technician?
- What is Recall metric? Can you explain it from scratch?
- What is R squared? Can it take negative values?
- Explain Bayes' theorem
- Explain hypothesis testing
- What is a Z-test? When would you use a Z test over a T test?
- How would you predict Samsung phone sales?
- How many orders of Fries does McDonald's sell in a year?
- Explain your process for doing A/B testing
2.4 Technical analysis questions
Meta data scientists work with one of the strongest data sets in the world. They are expected to have fluency in SQL (or equivalent) and in this interview you can expect mainly SQL-related problems.
You will be expected to work through your answers on a whiteboard (or online equivalent) and you should be well prepared to write SQL queries (with proper syntax).
In addition to SQL questions, you should also be ready for questions related to data structures, and algorithms, although these questions are less frequently asked (data scientists tend to have fewer engineering responsibilities at Meta than they do at other companies).
Finally, we recommend reading this guide on how to answer coding interview questions and practicing with this list of coding interview examples in addition to those listed below.
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?
- 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?
2.5 Behavioral questions
In addition to the question types outlined above, you can also expect to be asked some behavioral or "resume" questions about your past work experience and your motivation for applying to Meta. 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 Meta'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.
Let's see some examples.
- Why Meta?
- Why data science?
- What do you do currently?
- Describe a data and analytics project you've worked on
- Tell us about your past experience, skills and interests
- What is your biggest weakness?
- What has been the biggest challenge you have taken on?
3. How to prepare
Now that you know what questions to expect, let's focus on how to prepare. Below is our four-step prep plan for Meta. If you're preparing for more companies than just Meta, then check our generic data science interview preparation guide.
3.1 Learn about Meta's culture
Most candidates fail to do this. But before investing a ton of time preparing for an interview at Meta, you should make sure it's actually the right company for you.
Meta 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 Meta (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 Meta's 6 core values and Facebook's hacker culture.
3.2 Practice by yourself
As mentioned above, you'll encounter five main types of questions at Meta: product interpretation, applied data, quantitative analysis, technical analysis, and behavioral. One of the most useful resources around comes straight from Meta themselves - they made a prep video with mock interviews for all four onsite rounds. You should watch it all, but for ease of reference, the timings are:
01:56: Applied data
12:15: Product interpretation
27:32 Technical analysis
44:15 Quantitative analysis
For the analysis case interviews (product interpretation and applied data), study our articles on how to crack product improvement questions and metric questions, as well as how to crack data science case studies. We created the product improvement and metric guides for product managers but you should find a lot of the content pretty helpful. We also recommend reading up on Meta's products, as it will help to be familiar with how they work.
For quantitative analysis questions, we'd recommend brushing up on statistics fundamentals. Brilliant.org offers online courses designed around statistical probability and other useful topics, some of which are free. Search for specific questions and answers around statistics, machine learning, data analysis, and others on StackExchange.
For the technical analysis questions, your biggest priority should be to practice with example questions, especially the SQL questions. To help with that, we'd recommend reading this analysis of the 3 "types" of SQL problems.
For behavioral 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 2.5 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 Meta 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.
3.4 Practice with ex-interviewers
Finally, you should also try to practice data science mock interviews with expert ex-interviewers, as they’ll be able to give you much more accurate feedback than friends and peers.
If you know a data scientist or someone who has experience running interviews at Meta 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.
Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can practice 1-on-1 with ex-interviewers from Meta and other leading tech companies. Learn more and start scheduling sessions today.
