Facebook Data Science Internships: What to Expect & How to Land the Job

Facebook data science internships are some of the most coveted intern positions in data science.

That’s due in part to the incredible work experience Meta interns receive. Facebook data science interns gain hands-on training and work on actual data science problems. As a result, a Facebook internship instantly makes your resume more attractive and can help to launch your data science career.

Yet, Facebook’s data science intern program is one of the most competitive. Just 200 to 300 interns are accepted for summer programs and even fewer in the fall. Therefore, to land the job, you have to nail the interview and have the right skills.

We wanted to outline what you can expect as a data science intern at Facebook. This article will answer your most basic questions like:

• What do Meta data science interns do?
• How much do data science internships pay?
• And how do you land a data science internship at Facebook?

At Facebook the internships vary from year to year, so it is better to look at the Facebook Career Page for the latest open opportunities. Some of Facebook’s internship programs we’ve seen in the past include:

1. Enterprise Engineering Internship
2. Software Engineering Internship
3. Data Science Internship
4. UX Researcher Internship
5. Content Design Internship

As a Facebook data science intern, you will be assigned a mentor, who will guide you on many tasks ranging from data extraction to data analysis to visualization. Ultimately, the work that you do is determined by your experience.

Junior-level internship programs focus on tasks like data analytics, A/B testing, UX testing and more. For example, LinkBench, Meta’s database benchmark, was developed by a data engineering intern.

Although Facebook’s data science programs will provide ample learning time, they’re also tailored to aspiring data scientists who can develop solutions and contribute. As an intern, you won’t be a coffee-getter. Instead, you’ll develop real-world solutions and work on 1-2 data science projects during your internship.

As a Junior data science intern at Facebook you will be expected to:

• Work closely with other teams to propose logical strategies to facilitate the decision making process in the company.
• Build algorithms and design experiments to merge, manage, interrogate and extract data to supply tailored reports to colleagues, customers or the wider organization.
• Use machine learning tools and statistical techniques to provide solutions to problems.
• Test data mining models to select the most appropriate model for use on a project.
• Maintain clear and coherent communication, both verbal and written, to understand data needs and report results.
• Create clear reports that tell compelling stories about how customers or clients work with the business.
• Assess the effectiveness of data sources and data-gathering techniques, while offering improvements to their data collection methods.

Experienced interns, who are usually PhD candidates, work on complex data science projects and research, developing and testing new machine learning approaches, advanced analysis and attribution modeling, and more.

Probability & Statistics

Data Science is about using capital processes, algorithms or systems to extract knowledge, generate insights, and make informed decisions from data. In these cases, the acts of making inferences, estimating, or predicting form an important part of Data Science.

Multivariate Calculus & Linear Algebra

Most machine learning, especially in data science models, is built with several predictors or unknown variables. A knowledge of multivariate calculus is significant for building a machine learning model.

Programming, Packages and Software Applications

Data Science is essentially all about programming. Programming Skills for Data Science bring together all the fundamental skills needed to transform raw data into actionable insights. While there is no specific rule about the selection of programming languages to learn, Python and R are the most favored ones.

Data Wrangling

Often the data a business acquires or receives is not ready for modelling. It is therefore imperative to understand and know how to deal with the imperfections in data.

Database Management

Data scientists are often masters a wide array of skills, multidisciplinary in their approach. They have to know math, statistics, programming, data management, and visualization techniques to be a “full-stack” data scientist.

Data Visualization

What does data visualization necessarily mean? It is most typically a graphical representation of the findings from the data under consideration. Visualizations effectively communicate and lead an end-user to conclusions that a spreadsheet of data would struggle to quickly convey.

Machine Learning / Deep Learning

If you work with a company that manages and utilizes vast amounts of data, where the decision-making process is data-centric, it may be the case that a demanded skill is Machine Learning (ML). ML is a subset of the Data Science ecosystem just like Statistics or Probability, and equally contributes to the modelling of data and obtaining results.

In terms of pay, Facebook internships have some of the highest salaries in the industry. These aren’t unpaid or low-paid positions, Facebook interns earn on average $8,000 a month, according to a new report by Glassdoor.

For context, if interns were to work a full year, they would make $96,000, which is comparable to entry-level data scientist salaries.

You can apply for a position at Facebook online through their career page. Some universities are also lucky enough to bring representatives on campus, where recruiters can conduct face-to-face interviews. The Facebook interview consists of two rounds:

Technical interview: This takes place online and is about 60-75 minutes long. You are given a link to an online collaborative editor, where you code while talking to your interviewer. During the first 15 minutes, your interviewer goes through your resume and asks you about your projects and career. The next 60 minutes are spent solving 2 coding questions. The first question is generally easier. The last 5 minutes are for you to ask the interviewer any questions you may have about life at Facebook.

Onsite interview: This can also happen online instead of onsite depending on your location and the circumstances, especially in the post-COVID environment. If your interview is onsite, have fun! You’ll get a tour of Facebook’s headquarters and have the chance to talk and network with a host of current employees. The coding part has the same structure as interview one, but it is longer with harder questions. There will be design and behavioral questions also asked in the second round.

Data science interview questions tend to fall into four main categories, including data analysis, product sense, statistics and data modeling, and your interviewer can pick any question at random for you to answer.

The key is to prepare for a variety of different questions, with a main focus in preparation on the skills you’re weakest in. Some example Facebook data science internship interview questions include:

1. Given an integer array, move all elements that are equal to 0 to the left while maintaining the order of other elements in the array.

2. Given a list of intervals, merge all the overlapping intervals to produce a list that has only mutually exclusive intervals.

3. Given the head pointers of two linked lists where each linked list represents an integer number (each node is a digit), add them and return the resulting linked list.

4. Given two sorted linked lists, merge them so that the resulting linked list is also sorted.

5. Convert a binary tree to a doubly linked list so that the order of the doubly linked list is the same as an in-order traversal of the binary tree. After conversion, the left pointer of the node should be pointing to the previous node in the doubly linked list, and the right pointer should be pointing to the next node in the doubly linked list.

6. Given a binary tree and a number ‘S’, find all paths from root-to-leaf such that the sum of all the node values of each path equals ‘S’.

7. Given a dictionary of words and an input string tell whether the input string can be completely segmented into dictionary words.

8. Given a list of daily stock prices (integers for simplicity), return the buy and sell prices for making the maximum profit. We need to maximize the single buy/sell profit. If we can’t make any profit, we’ll try to minimize the loss.

9. Given a double, ‘x’, and an integer, ‘n’, write a function to calculate ‘x’ raised to the power ‘n’.

10. Serialize a binary tree to a file and then deserialize it back to a tree so that the original and the deserialized trees are identical.

One of the best ways to land a Meta internship is through a referral. Referrals can help you get your foot in the door. Yet, there are many other tips that you can follow, including:

1. Thoroughly study the company’s core values and mission statement
2. Have examples of data science projects you’ve worked on available
3. Integrate the company’s values into your answers
4. Talk to people in the industry to figure out what each of the roles entails
5. Find a mentor and work with your mentor in interview prep and for improving your skills
6. Choose an internship that’s suited to your field of study, skills and interests
7. Do all the exercises and assignments provided by the recruiter
8. Work on a few open data sets and apply your learning. Even if you don’t understand the math behind a technique initially, work to understand the assumptions, what it does, and how to interpret the results.

Bottom line, data scientist internship programs are highly competitive and require extensive prep to land. You can’t expect to do the bare minimum. You have to have an impressive resume, intermediate-to-advanced skills, and you must nail the interview to work as a data science intern at Facebook.

Interview Query offers a variety of learning resources to help advance your career. Check out these resources:

• Data Science Course
• 500+ Data science interview questions