Advice > Data

Meta Data Scientist Interview (Process, Questions, Prep)

By Jeanette Patindol with input from the following coaches: Enming L . November 11, 2024
Meta or Facebook data scientist interview

Data scientist (product analytics) interviews at Meta 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 this 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. Meta Data Scientist (Product Analytics) Role and Salary

Before we cover your Meta data scientist interviews, let’s first look at the role itself.

1.1 What does a Meta Data Scientist do?

Data scientists at Meta are responsible for processing, analyzing, and interpreting data sets and using them to evaluate and make recommendations to improve Meta’s various products. They’re vital to the company’s optimization and decision-making process.

Specifically, according to Meta, you’ll be doing the following if you’re in charge of Product Analytics, which is what most Meta Data Scientist job posts involve:

  • Use quantitative tools to uncover opportunities, set team goals, and work with cross-functional partners to guide the product roadmap.
  • Explore, analyze, and aggregate large data sets to provide actionable information and create intuitive visualizations to convey those results to a broad audience.
  • Design robust and informative experiments, considering statistical significance, sources of bias, target populations, and potential for positive results. 
  • Collaborate with engineers on logging, product health monitoring, and experiment design and analysis.
  • Partner with data engineers on data infrastructure—tables, dashboards, metrics, and goals.
  • Drive product decisions via actionable insights

As a Meta data scientist, you’ll be integrated into what is known as a Meta Pod, which consists of software engineers, designers, product managers, data engineers, data analysts, and other functions depending on the product or service. 

It is your responsibility to use your analytics expertise to determine which opportunities to work on. You’ll be working closely with data engineers to gather data sets to extract insights from. 

You’ll also be the one to come up with the proper metrics to measure your team’s progress and meet your goals, in collaboration with the product team. 

You’ll likewise work closely with software engineers (SWEs) on experiments, from designing to monitoring and analyzing them.

Because Meta is very product-oriented, data scientists are required to have good product sense. At the end of the day, as a Meta data scientist, your responsibility is to use your technical expertise to deliver insights that will help improve user experience on Meta’s products.

What skills are required to be a Meta Data Scientist?

Based on an analysis of the current data scientist posts at Meta, the minimum educational requirement for a data scientist depends on the specialization. 

Some posts will only require a Bachelor’s degree in Mathematics, Statistics, or a related technical field, while others—like Product Analytics—require a Master’s degree in a quantitative field such as Computer Science, Economics, Engineering, Information Systems, Analytics, Mathematics, Physics, or Applied Sciences. 

Having at least 4 years of relevant work experience is a must.

Experience working on experimental design and using data querying languages (e.g., SQL), scripting languages (e.g., Python), and/or statistical/mathematical software (e.g., R) are also highly sought after. 

Excellent data visualization and stakeholder communication and presentation skills are also part of the minimum skills requirement. Some posts will require experience with predictive models.

If you have all these technical skills and have been able to use them to drive business decisions in a previous role, you’re a great candidate

1.2 How much does a Meta Data Scientist make?

Based on the computations from Glassdoor data, the average data scientist base salary at Meta is $170K/year, which is 46% higher than the estimated average base salary of a data scientist in the US at $116K/year.

Location also plays a part in the difference in salary based on Glassdoor data. To compare:

  • Meta India Data Scientist: est. average of $11K/year base pay
  • Meta US Data Scientist: est. average of $170K/year base pay

Below you can see the average salary and compensation of the different data scientist levels at Meta US, as of early 2024, based on Levels.fyi.

While we presume that you already know which specific level you are applying for, it’s still good to double-check this with your recruiter. Your recruiter should be able to advise you on which level you’re being evaluated.

Ultimately, how you do in your interviews will help determine what you’ll be offered. That’s why hiring one of our ex-Meta interview coaches can provide such a significant return on investment.

And remember, compensation packages are always negotiable, even at Meta. So, if you do get an offer, don’t be afraid to ask for more. If you need help negotiating, use this salary negotiation guide and consider booking one of our salary negotiation coaches to get expert advice.

2. Meta Data Scientist Interview Process and Timeline

2.1 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:

  1. Resume screen
  2. Initial screen (~45 minutes)
  3. Full Loop round (4 interviews, 45 minutes each)

Let's look at each of these steps in more detail below:

2.1.1 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-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. According to Glassdoor, 25% of candidates who got interviews with Meta were through employee referral. So, 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.

2.1.2 Initial 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’s already a very focused 45-minute phone screen.

There are four focus areas during the initial screen:

1. Programming: assesses your ability to develop solutions to complex data problems using programming and scripting languages (any type of SQL is acceptable). You will be assessed based on your familiarity with common data manipulations, such as merging data sets, filtering data to find insights, handling missing data, and making coding decisions.

For example, Meta interviewers might give you the following data:

  • An attendance log for every student in a school district attendance_events : date | student_id | attendance 
  • A summary table with demographics for each student in the district all_students : student_id | school_id | grade_level | date_of_birth | hometown 

      Then, using this data, Meta interviewers could ask you questions like the following: 

  • What percent of students attend school on their birthday? 
  • Which grade level had the largest drop in attendance between yesterday and today?

2. Research design: assesses how you identify and design appropriate testing and analysis to determine relationships between relevant variables that answer strategic questions

For example, you could be asked questions on the following:

  • How would you design an experiment to prove/disprove something? 
  • If an experiment won’t work, then what’s an alternative? 
  • What are the downsides of the methodology you propose? 
  • Are there biases in the analysis or experiment that we should correct for?

3. Determining goals and success metrics: assesses your ability to identify metrics that reflect operational success and contribute to the achievement of business objectives

Your Meta interviewers could ask you the following questions:

  • How would you measure the impact or value of something? 
  • What metrics or counter-metrics would you assess when trying to solve business problems related to our products? 
  • Focusing on a specific dataset, what are the metrics you would actually look at?

4. Data analysis: assess how you leverage methods to answer exploratory and hypothesis-based questions that inform business decisions. These methods can range from descriptive statistics to measurement models.

Your Meta interviewers could ask you questions along these lines:

  • How would you prove a hypothesis is true? 
  • What are the hypotheses that would lead to a decision? 
  • Can you translate concepts generated into a specific analysis plan? 
  • Are you able to use data to answer the original question posed with enough detail to demonstrate the ability to execute on an analysis?
  • Can you lay out the steps clearly and tie this back to answering the question?

For more information on how to prepare for these, check out this Meta initial phone screen guide for data scientists.

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. 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.

2.1.3 Full Loop round

The final stage in the interview process for Meta's data scientist candidates is what Meta calls its Full Loop round of onsite or video conferencing interviews. As outlined by Meta's very useful Full Loop guide, this typically includes four interviews of 45 minutes each, consisting of:

  • Technical skills. This is a coding interview where you'll analyze an open-ended product problem and try to solve it through code. The interviewer will assess your performance in programming, communicating effectively, data analysis, and determining goals and success metrics.
  • Analytical execution. In this interview, you’ll be assessed on your ability to create hypotheses for launching new products, your knowledge of quantitative analysis, how you determine goals and success metrics, and how you demonstrate agility.
  • Analytical reasoning. In this interview, your research design, analytical design, data visualization, storytelling through data, and setting goals and success metrics will be evaluated.
  • Behavioral. This is to test whether you’re a good fit for the company and the position, given your past experiences and your answers to hypothetical questions on what you might encounter at Meta. Interviewers will particularly look for how you demonstrate agility, grow continuously, partner with stakeholders, build inclusion, and communicate effectively.

For your technical skills interview, 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 Full Loop interviews tend to be more difficult than the questions from the initial screen. So, be sure to double down on your preparation for them!

2.2 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 initial screen, 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 Full Loop, 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 Full 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.

3. Meta Data Scientist Example Questions 

Now that we've covered the process, let's get into the kinds of questions you can expect for each type of interview. Bear in mind that the initial screen draws from the same question types as the Full Loop interviews, just in less depth.

Note that many of the questions below are asked in the form of case studies. To learn more, read our guide on data science case study interviews.

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.

3.1 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 since 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.

Meta data scientist interview question examples: Technical analysis

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?
  • Using SQL, how would you provide a distribution of rolling 7-day average money spent per person, broken up into categories of purchase?
  • In SQL, how do you combine two datasets while keeping all the information?
  • How can you pull the unique conversation events from a database in SQL?
  • How do I create a validation tool for Facebook Marketplace?
  • Deep dive into a specific project using SQL, Python, and statistics.

Data structure and algorithms

  • There is an algorithm that rates posts based on their likelihood of being 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.
  • How do you revert a string?
  • Merge these two tables and select a row where this condition is true.

3.2 Analytical execution 

At the end of the day, Meta's data scientists help to drive product and business decisions. They need to be able to use their analytical skills to solve real-world business problems and contribute to the overall success of the team and company.

With that in mind, the analytical execution interview is meant to evaluate you on how you will use your hypothesis creation skills and your knowledge of core statistical concepts for data-driven problem-solving and other business decisions.

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.

In this interview, you’ll be assessed on four key areas:

  • Creating hypotheses: your ability to create hypotheses for launching new products and problem-solving; working knowledge of core statistical concepts: Law of Large Numbers, Central Limit Theorem, Linear Regression, Bayer’s Theorem
  • Quantitative analysis: your ability to quantify a feature’s tradeoffs in terms of metrics
  • Setting goals and success metrics: your ability to determine goals and create metrics
  • Agility: your ability to adapt to data changes and challenges

Let's take a look at some questions.

Meta data scientist interview question examples - Analytical execution

  • How would you measure the success of a product?
  • What KPIs would you use to measure the success of the newsfeed?
  • How would you improve notifications?
  • How will you separate “high quality” notifications from all notifications?
  • Activity in Facebook user groups is down by 20%; what do you do?
  • Friends acceptance rate decreases 15% after a new notification system is launched—how would you investigate?
  • The notification product will launch a new feature. The feature is a new type of notification. When your friends attend an event, you will get a notification. How do you measure the success of this new feature?
  • Imagine a product similar to Facebook Marketplace called Facebook Restaurants. Measure the success of this new feature.
  • How would you measure the success of a newly released feature that is similar to the Facebook group chat?
  • How will you gauge the success of the new notification feature in Groups? Provide guardrail metrics.
  • Should we launch a new notification feature? if we launch, how do we evaluate the performance?
  • How would you build a “restaurants you may like” recommender system on the news feed?
  • 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 Facebook Marketplace? How can you tell if your model is working?
  • You see "average reels watched" has dropped precipitously suddenly, how would you figure out what's happening?
  • Given a case study about a new group call feature Facebook is developing, how do you test if that would be a success?
  • Talk through the challenges that are involved with bringing a solution to production.

Enming, an interview coach for data scientists, qualifies, “Analytical execution is essentially about product sense. Key words to focus on are metric design, metric measurement, A/B tests, and providing insights and suggestions to the team.” 

3.3 Analytical reasoning 

For the analytical reasoning interview, you’ll be evaluated on how you structure ambiguous product questions and how well you design experiments to test hypotheses, pinpointing the best data sets for specific product questions. 

You’ll also be assessed on your understanding of the downsides and biases of certain methodologies and how you plan to handle them. Lastly, your ability to extract relevant insights and tell a story through data will be tested as well.

Let's take a look at some example questions.

Meta data scientist interview question examples - Analytical reasoning

  • How can you find out the xxx feature on the platform? What data would you look at?
  • Can you find the first login for a platform, given a list of users?
  • How would you do an A/B test on your new metric to see if it truly captures meaningful social interactions better?
  • Explain your process for doing A/B testing.
  • What are you going to do with network effect when designing an A/B test?
  • How would you estimate how much fake news is on Facebook? How would you estimate its impact?
  • How would you use data to confirm that users’ high school data is real?
  • 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 determine the health of Facebook Groups?
  • How would you determine if a new system that identified and banned accounts that were posting ads for prohibited content was working?
  • Your product manager is launching a new feature to improve the engagement on the newsfeed; how would you guide her on whether the overall impact is positive? How would you recommend setting up an experiment?

3.4 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, how you would react to hypothetical situations you might encounter at Meta, and your motivation for applying. 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. 

According to the Meta Data Scientist Ful Loop interview guide, when answering behavioral questions, be sure to demonstrate how you:

  • Operate in ambiguous and undefined projects.
  • Move quickly and resourcefully.
  • Can be open about your failures and talk through examples of what you’ve learned from them.
  • Build relationships and collaborate with your direct and partnering teams to achieve mutual objectives.
  • Influence and get buy-in from peers who may be resistant to your goals.
  • Exhibit introspection and self-awareness.

Let's see some examples.

Meta data scientist interview questions - Behavioral

  • 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?
  • Tell me about a time when you had to influence a stakeholder on a decision they don’t necessarily agree with.
  • Tell me about some work you are proud of.
  • How would you handle ambiguity? How do you make recommendations in the face of ambiguity?
  • What do you like about work? What do you dislike?
  • Tell me about something challenging you are working on.
  • Give an example of how you used your skill to gain insight into a difficult problem.
  • Tell me about your best learning in any of your previous projects and how it was unique in terms of use case.

4. Meta Data Scientist Interviewing Tips 

You might be a fantastic data scientist, but unfortunately, that won’t necessarily be enough to ace your interviews at Meta. Interviewing is a skill in itself that you need to learn.

Let’s look at some key tips to make sure you approach your interviews in the right way.  

4.1 Ask clarifying questions

Often the questions you’ll be asked will be quite ambiguous, so make sure you ask questions that can help you clarify and understand the problem. Most of the questions will focus on testing your technical proficiency.

4.2 Be conversational

Meta wants to know if you have excellent communication skills. So make sure you approach the interview like a conversation. 

Meta will also be testing you on your ability to tell a clear and concise story through data, especially to stakeholders who may or may not have a technical background. So be sure to brush up on your basics and practice interpreting them in a way that’s clear and easy for everyone to understand.

4.3 Think out loud

You need to walk your interviewer through your thought process before you actually start coding. Meta recommends that you talk even while coding, as they want to know how you think. 

Your interviewer may also give you hints about whether you’re on the right track or not. Be alert for these, and be ready to pivot once you’ve gotten the prompt. This shows you’re eager to learn and listen well to feedback.

4.4 State and check assumptions

You need to explicitly state assumptions, explain why you’re making them, and check with your interviewer to see if those assumptions are reasonable. 

4.5 Present multiple possible solutions

Present multiple possible solutions if you can. Meta wants to know your reasoning for choosing a certain solution. 

When dealing with complicated or ambiguous questions, show your ability to deconstruct such problems into groups and demonstrate how you can combine these groups for your proposed solution.

4.6 Be honest and authentic

Be genuine in your responses. Meta interviewers appreciate authenticity and honesty. If you faced challenges or setbacks, discuss how you improved and learned from them. When talking about failure, don’t try to hide your mistakes or frame a weakness as a strength. Instead, show what you learned and how the failure helped you grow.

4.7 Center on Meta’s culture

Familiarize yourself with Meta’s core values and align your behavioral responses with them. Meta values certain attributes such as comfort with ambiguity, agility, collaborative nature, and a sense of urgency.

4.8 Brute force, then iterate

When coding, don’t necessarily go for the perfect solution right away. Meta recommends that you first try and find a solution that works, then iterate to refine your answer.

4.9 Keep your code organized

Make sure to keep your code organized so your interviewer won’t have a hard time understanding what you’ve written. Meta wants to see that your code has captured the right logical structure.

5. Preparation Plan

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 general data science interview preparation guide.

5.1 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.

Meta recommends checking out these resources to help you learn more about the company:

In addition, we would recommend reading the following:

5.2 Practice by yourself

As mentioned above, you'll encounter four main types of questions at Meta: technical skills, analytical execution, analytical reasoning, and behavioral. 

For the analytical execution and analytical reasoning interviews, 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.

We also recommend brushing up on statistics fundamentals for quantitative analysis questions, which you will encounter in your analytical execution interview. 

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

Meta recommends these resources for your analytical and technical interview prep:

Analytical prep:

Technical prep:

For behavioral questions, we recommend reading our guide to Meta behavioral interview questions, where you’ll find a step-by-step method for answering them. You can then use that method to practice answering the example behavioral questions provided in Section 3.4 above.

Once you’re in command of the subject matter, you’ll want to practice answering questions. But by yourself, you can’t simulate thinking on your feet or the pressure of performing in front of a stranger. Plus, there are no unexpected follow-up questions and no feedback.

That’s why many candidates try to practice with friends or peers.

5.3 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.

However, be warned, as you may come up against the following problems:

  • It’s hard to know if the feedback you get is accurate.
  • They’re unlikely to have insider knowledge of interviews at your target company.
  • On peer platforms, people often waste your time by not showing up.

For these reasons, many candidates skip peer mock interviews and go straight to mock interviews with an expert. 

5.4 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. 

In our experience, practicing real interviews with experts who can give you company-specific feedback makes a huge difference.

Find a Meta data scientist interview coach so you can:

  • Test yourself under real interview conditions
  • Get accurate feedback from a real expert
  • Build your confidence
  • Get company-specific insights
  • Learn how to tell the right stories, better
  • Save time by focusing your preparation

Landing a job at a big tech company often results in a $50,000 per year or more increase in total compensation. In our experience, three or four coaching sessions worth ~$500 make a significant difference in your ability to land the job. That’s an ROI of 100x!

Click here to book Meta data scientist mock interviews with experienced interviewers. 

 

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