Your guide to landing a data science role at Facebook — covering everything you should know before applying, and a look into the interview process, and more.
The data science role at Facebook is an enviable one. Data professionals not only have the chance to utilize one of the world’s largest datasets and do work that impacts the social media network’s roughly 2.89 billion monthly active users; they also earn approximately 23% more than the national average salary for a data scientist in the US.
Before we get into it...
80% of hiring at Facebook is through sourcing or referrals, so if you want to get in, reaching out to your network 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.
This article will be broken down into 5 parts:
The data science role at Facebook combines strong analytical and technical skills with sharp product sense.
Roles and responsibilities vary greatly depending on which team you join; however, we compiled some of the more generalized responsibilities from a range of active data science job descriptions at Facebook to give you a general idea of what to expect.
Promotions and bonus compensation are strictly tied to how well you do in your performance review. Salaries are determined by a formula based on job category, experience level, and location--leaving little room for negotiation.
Following are the average salaries for data scientists at Facebook in the Bay Area:
The above data is taken from our offer review community. The data updates in real-time as new offers are added. To see the range of salary data, and to get specific data to your location, visit our community.
Since your resume is what gets you noticed by a recruiter, you want to make sure you do it right. In doing so, you must keep in mind that recruiters are only going to look at your resume briefly — probably not more than 30 seconds.
You've probably heard that your resume shouldn't be longer than 1 page long; this advice still holds true. If you have more than 10 years of relevant job experience, then adding a second page is generally okay, but if you have less than this, you shouldn't exceed a page.
The idea is twofold:
👉 Want help with formatting your resume? Read next: Which resume outline should you use?
There are typically 4 sections you should include:
👉 Another helpful resource: How far back should your resume go? Strategies for every career stage
Data science interviews at Facebook are challenging. The emphasis on product-specific questions means you should familiarize yourself with some of the business problems Facebook data scientists solve on a daily basis.
Topics you can expect to cover include algorithms, coding (nearly 50% of all questions in the interview), product, statistics, system design, modeling, probability, and business case studies.
The first step is a phone screen with an HR recruiter. However, be prepared to answer more than just the typical getting-to-know-you questions about your resume. Don’t be surprised if you hear questions about SQL and product analysis.
It's 45 minutes long and consists of product analysis and technical questions.
Product analysis questions
These are product-specific questions related to the role you applied for. Case study questions test your product sense and ability to create solutions to business problems.
Expect questions about experimentation (eg: how would you roll out ‘X’ feature and how would you measure its success?) and what metrics you would use as key performance indicators.
During the technical screen, you will be judged on your approach to solving problems, your ability to articulate and prescribe solutions, and justify your reasoning.
You’ll be given a series of business case questions and tasked with finding a solution using Python, SQL, and other quantitative analysis tools.
The final round in the hiring process consists of four interviews lasting 30 minutes each. You’ll revisit some of the topics you covered during the screening process but in greater detail.
According to Facebook’s Onsite Interview Guide, compiled by recruiters and data scientists at Facebook:
“Throughout your discussions during the day, your interviewers will be assessing your ability to tell a compelling story with data, make data-driven decisions, and impact change through product development and optimization.”
You’ll be presented with a product case study focused on interpreting user behavior using data and metrics. Product interpretation questions test your ability to translate users’ behavioral data into product ideas and insights.
Expect open-ended questions such as: “How would you evaluate YouTube’s video recommendations?”
You will be expected to:
The data science job at Facebook, unlike other tech companies, has a 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.
This interview focuses more on the technical side of solving a problem using data.
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.
You’ll be given a more specific product-related problem and asked to do the following:
While this section won’t cover advanced stats and math concepts, you’ll need to use basic stats to evaluate quantitative reasoning and applied statistics. Your interviewer will be assessing your knowledge of relevant mathematical, probabilistic, and statistical concepts and how they relate to Facebook products.
The final round involves a coding interview where you’ll illustrate your solution on a whiteboard or online equivalent. Your interviewer will be assessing your ability to talk through the intention of your code and the logic behind it. The questions will challenge you to analyze open-ended product problems with code.
Prepare to field questions on data structures, algorithms, and writing SQL queries.
Here's the evaluation process after your interview:
The hiring committee generally sides with the recommendations made during a candidate review meeting, so the last step of the process is a mere formality.
Given that Facebook is a mammoth company--the social network had 58,604 full-time employees as of December 2020--data scientists work in a range of capacities, including engineering, research, and data analytics (product focus).
Product-focused roles at Facebook involve using data analysis techniques to understand customer profiles and test new features.
Data scientists who work on Whatsapp, Oculus, Messenger, or Instagram:
For example, they’ll use data insights to assess how users are responding to a new notification feature on Facebook or analyze usage metrics on Whatsapp to create geotargeted ads. Some data scientists also work with user segments, such as small business owners. Data scientists in the Small Business Group help small businesses connect with customers on Facebook and promote their businesses through solutions like pages, advertising, and offers.
Core Data Science is an R&D team that works to improve Facebook’s product, infrastructure, and processes. This interdisciplinary team employs people with expertise in computer science, machine learning, and statistics, as well as economics, political science, and sociology.
For example, data scientists created a data visualization that shows the reach of Facebook friends across European cities, which goes to show how small the world really is. Engineering-focused projects use AI and machine learning to improve the user experience on Facebook products. In 2017, data scientists created the Automatic Alt-Text (AAT) tool, which enables screen readers to recognize the contents of most images in News Feed.
One Facebook data scientist reports:
"I really enjoy working with the people at Facebook. Their work ethic is awesome, and everyone is self-driven. There is a lot of trust and autonomy; it is entirely up to me how I plan my day to meet my project goals. It could be a bit daunting at times, but there is a great support system at the company."
Employees on Glassdoor echo this positive review, giving the Facebook Data Scientist role an average of 4.3 out of 5 stars. Positive reviews indicate good pay and benefits, a lot of support from peers, and interesting work with a lot of autonomy. Bad reviews, unsurprisingly, bring up the poor work-life balance that is quite often associated with working at Facebook.
Employees tend to attribute the poor work-life balance to working in a competitive environment where the expectations from the people around you are high. Facebook is also known for having a quick promotion trajectory, and many are willing to let their WLB suffer to move up the ladder quickly. And as with most roles at Facebook, your WLB also heavily depends on which team you join.
Generally speaking, if you don't care about being promoted as quickly, you can have a better work-life balance. But if progression is important to you, employees report sometimes working over 60 hours per week as a data scientist.
If you get a data scientist role at Facebook, you will likely start out as an individual contributor (IC) somewhere in the IC3-IC5 range.
Compared to other tech giants, Facebook's promotion trajectory is fairly quick. If you start out at IC3, you can expect to move up to IC5 within a few years if you meet the expectations. Moving above IC5, however, is much harder and most data scientists at Facebook do not get promoted to IC6+.
If you are able to move above IC5, you'll have 2 possible tracks:
Facebook's performance reviews currently happen twice a year, but beginning in 2022, they will happen once a year. A stack ranking system is used where managers grade their employees on a bell curve. Then, employees complete a self-assessment and get peer feedback in order to receive their final performance grade.
👉 Read next: Behind Performance Reviews + Bonuses at Facebook
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