How to prep, interview, and get the job. We'll walk you through it step- by- step (with extra resources to help you along the way).
Working as a data scientist at a big company is a dream come true for many. However, before pursing this role, it's important to understand that Facebook sees data science a bit differently, even compared to other FAANG companies.
So, to start, it might be helpful to define the 4 core areas Facebook data scientists work in. This will frame everything in your interview process:
Does this sound like something you'd be interested in?
If so, start reaching out to your network. 80% of hiring at Facebook is through sourcing or referrals, so if you want to get in, this is your best bet.
Leverage your immediate network and second degree connections to score a referral. It doesn't matter who refers you for the role. In fact, there is a whole team at Facebook dedicated to helping referrals, and the process there is fairly fast-paced: some candidates get a recruiter call within a week.
Once you get that recruiter phone call, what happens next?
It's 45 minutes long and primarily consists of technical interview questions. In this interview, they're looking for two things:
How you engage with the problem, including your thought process, structure, and communication style, are even more important than your ability to solve the problem.
Facebook recommends spending some time understanding what they consider a "Facebook product":
"Spend some time engaging with Facebook Products less as a user and more as someone who is tasked with improving or developing these products. The “What We Build” tab on this link outlines what we consider a “Facebook Product” (Ads, Mobile, Timeline, News Feed, Messaging, etc.). Note: It isn’t a complete list"
In addition to this research, you should always look into interview questions - you can try Candor Community for recent data.
In this first data science interview, Facebook looks for 4 factors to move you forward to the next round:
1. Structure: How good you are at taking a large problem or open ended question and framing it in the right context.
2. Action: Does your review lead to specific action items for the team? This should show what working with you is like.
3. Analytical Understanding: Can you translate between numbers and words (i.e. prove to your interviewer that product “X” should be built through data resulting in analytical proof)?
4. Hypothesis Driven: Can you identify reasonable hypotheses and apply basic logic to support those hypotheses?
Typically, candidates for the data science role will be invited for an onsite interview during the final round. This could take place in Menlo Park, Seattle, or New York. During COVID, however, you will likely do this interview via video. In this final round, you'll be tested on your product knowledge, especially if you're on the product analyst track.
This will involve a case study focused on making sense of user behavior using data, analytics and metrics. Questions in this section are generally broad, like:
When answering these questions,
The data science job at Facebook, unlike other tech companies, has broad scope. Many job seekers assume this interview is just about jamming on some sql/ python, but it's not. The case study will really test your ability to think about the business and answer questions about how a feature or metric can affect the user experience. You will also get related questions around trade-offs and how they may affect revenue or engagement.
👉 Try your hand at recently asked questions on our Mock Interview platform.
Technical questions will focus on solving a problem, using a dataset Facebook will provide during the interview. This will often include data pertaining to Facebook products, so it really helps to understand those ahead of the interview. Specifically, think through how the products came to be, what decisions were made along the way, and what metrics matter.
Occasionally, you will be asked a question like:
You will then be expected to:
Don't forget to consider details like a/b testing, thinking of technical tradeoffs - don't just focus on the data analysis. Here are some questions Facebook recommends focusing your mind on while you structure a solution:
If you want to prep more, we deeply recommend using genuine Facebook data scientist interview questions and spending time understanding how the salary negotiation process works at FANG.
Find out how much you’re worth and how to ask for more — the right way.