Meta Data Scientist Interview (Process, Questions, Prep)

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.

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.

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.

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.

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:

• About Meta
• Meta’s mission statement
• Meta's 6 core values 
• Meta Newsroom
• Meta Careers
• Meta Life
• Meta Diversity
• Meta Employee Benefits
• Interviewing at Meta blog
• How to get users and grow (by Alex Schultz, VP of Growth at Meta)
• How Facebook Used Science And Empathy To Reach Two Billion Users (by FastCompany)

In addition, we would recommend reading the following:

• Facebook's hacker culture (by Mark Zuckerberg, via Wired)
• Meta annual reports and strategy presentations (by Meta)
• Meta's approach to tech trends (by CB Insights)
• Meta org culture analysis (by Panmore Institute)

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:

• How Experimentation Informs Product Development: LinkedIn
• The Pitfalls of A/B Testing in Social Networks
• Khan Academy Statistics & Probability Course
• Cracking the PM Interview by Gayle Laakmann McDowell

Technical prep:

• SQL Course
• Mode Analytics SQL Tutorials
• Programmer Interview SQL Practice Database
• Python | SQL Comparison
• Data Transformations in R

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.