Complete Guide: Facebook Data Science Interview Questions

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).

Facebook Data Science is Unique.

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:

  1. Use quantitative tools to uncover opportunities, set team goals, and work with cross-functional partners to guide the product roadmap.
  2. Explore, analyze, and aggregate large data sets to provide actionable information, and create intuitive visualizations to convey those results to a broad audience.
  3. Design informative experiments considering statistical significance, sources of bias, target populations, and potential for positive results.
  4. Collaborate with engineers on logging, product health monitoring, and experiment design/ analysis.

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?

The Interview Process: Early Stages

Step 1: Recruiter Phone Screen.

  • No Python or SQL questions here - this phone interview will mainly focus on your background, with a few behavioral questions peppered in.
  • Essentially, the recruiter is just looking to assess your communication skills and scope of your previous work.
  • Most candidates make it through.

Step 2: Video Interview with a Facebook Data Scientist.

It's 45 minutes long and primarily consists of technical interview questions. In this interview, they're looking for two things:

  1. Creativity and skill when solving business problems.
  2. How you articulate/communicate your solutions.

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.

Preparing for early stage interviews.

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.

Characteristics Facebook looks for.

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?

The Interview Process: The Final Round!

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. 

Product Interpretation Questions.

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:

How would you evaluate Facebook Groups?
How would you assess engagement on Instagram Stories?

When answering these questions,

  • You'll be expected to show scope: Can you put your data scientist hat aside for a moment and see the perspective of the PM and the rest of the team?
  • You will likely be asked follow ups: Commonly around adding new features, creating metrics, and testing hypothesis through designing appropriate tests.

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.

Applied Data/Analytics Interview.

This is the part of the data scientist interview you probably expected.

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:

Here's X card transaction dataset, how will you use machine learning to design a fraud detection algorithm?

You will then be expected to:

  1. Select the right data and draw inferences from it to make informed decisions
  2. Build metrics
  3. Circle back to how your decisions might impact the core product

Data scientists at Facebook are always expected to approach problems with broad scope and keen product sense.

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:

Why do you think they made certain decisions about how it works?
What could you do to improve the product?
What kind of metrics would you want to consider when solving for questions around a product’s health, growth, or engagement?
How would you measure the success of different parts of the product?
What metrics would you assess when trying to solve business problems related to our product?
How would you tell if a product is performing well or not?

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.


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