Top 22 Meta (Facebook) Data Scientist Interview Questions + Guide in 2024

The Meta data scientist is a unique, coveted role among MAANG companies. Generally speaking, the Meta data scientist is more of a product analyst, working more closely with business than engineering teams.

Considering their impressive earnings in 2023 and their outlook for 2024, we can expect more focus on feature and product development and exciting new challenges for data scientists at Meta.

Considering their data scientists’ impact on their business, it’s no wonder that the interview process is thorough and challenging.

We’ve compiled a comprehensive guide below to help you prepare for all the interview stages. Read on to learn more about each step of the process, commonly asked Meta Data Scientist interview questions, and our favorite tips to get hired! We’ve also listed some valuable resources for you at the end.

As we’ve already mentioned, the Meta data science role is slightly different from comparable ones in that it looks more like a product analyst job. You can expect questions that test your product and business sense and your mathematical skills, such as descriptive statistics, distributions, and probability. You should be fluent in topics like the central limit theorem, linear regression, and Bayes’ theorem. In addition, your ability to analyze open-ended problems using code will be assessed. Like Google, you can code in your preferred language, as the coding challenges primarily aim to test your problem-solving skills.

Finally, cultural fit is everything, so prepare for behavioral questions while keeping Meta’s core values and mission in mind.

Please note that the questions and structure of the interview process will differ slightly based on the team and function. Always read the job description carefully while preparing your interview plan.

On a related note, check out our guide on preparing a solid interview strategy, or read about this data scientist’s inspiring journey here if you’re feeling stuck.

The process generally has multiple rounds spanning a month or even two.

Step 1: Resume Screening

A recruiter will usually reach out to you on LinkedIn if they think your profile is suitable for an opening. If you want to be more proactive about applying, you can submit an online application. However, your chances of being noticed will increase if you get a referral from a Meta employee.

This is also arguably the most competitive stage, so ensure your resume is tailored to show your skills in the best light. Here are some tips to ensure your resume is in top shape:

• Tailor your CV to the job description as much as you can. Mirror their language to show you understand what is expected of you in the role.
• Quantify your project’s impact. Mention your contribution to the project, talk about the positive outcome, and show initiative and leadership.
• Use action verbs like “initiated,” “launched,” and “led.”
• Lead with your strongest points. If you have less work experience, start with your education, followed by relevant projects.

If you’re having trouble getting your foot in the door, let us help! We can review your resume here.

Step 2: Recruiter Screening

A 15-minute phone interview with a recruiter will be scheduled to get a sense of your work experience and skillsets. Don’t underestimate this step, as prospective Meta data scientists are often asked a couple of high-level SQL and product analysis questions in this round. They may also ask you why you want to join Meta or ask CV-based questions, so prepare some responses to help you sail through this important step.

Tip: Once you pass this stage, ask your recruiter for pointers on the next steps and if they have resources to guide you. They’ll likely be more than happy to help you prepare for the following stages.

Step 3: Technical Phone Screen

Next, you’ll have one or two 45-minute technical rounds, during which you’ll be given SQL and analysis case questions to solve. Some typical analysis case questions are: “How would you improve [X] product or feature?” or “Would you recommend that we double our ads on Instagram?”

Even though this round is more high-level than the following ones, be detailed in your answers, and don’t miss any edge cases.

Step 4: Onsite Interviews

If you do well in the online technical round, you will be invited for an onsite interview, where you’ll go through the four following rounds:

• Analysis case in product interpretation: This is a case study round that focuses on your product sense.
• Analysis case in applied data: This interview examines how you’d use data to solve a business problem. It will be more technical than the previous round and assess how you frame problem statements and solve them using available data.
• Quantitative analysis: The interviewer will test your knowledge of mathematical, statistical, and probabilistic concepts that relate to data science challenges encountered at Meta. You won’t be asked advanced mathematics like calculus, estimation problems, and brain teasers.
• Technical analysis: This is a coding interview where you’ll analyze an open-ended product problem through code.

Let’s examine the top questions Meta asks in their data science interviews. Before you check the solutions, try to solve the questions on your own. Remember that the interviewer will gauge how well you handle open-ended questions and how creative and articulate you are in thinking through these problems. It’s not about arriving at the perfect or correct answer but how you engage with the problem.

It’s helpful to engage with Meta products from the mindset of someone tasked with improving them. For behavioral questions, follow the STAR framework and research Meta’s culture and mission.

1. Describe a challenging data science project you handled.

You’ll make many complex decisions at Meta, so demonstrate your experience handling such situations.

How to Answer

Focus on a project you feel comfortable discussing in depth. Detail your approach, strategies, and impact. Be authentic and demonstrate that you worked collaboratively with your team and stakeholders. For more guidance, check out our insights on approaching project-based behavioral questions.

Example

“I led a project to optimize investment strategies in my previous firm. The challenge was integrating disparate data sources while ensuring model accuracy. My approach involved collaborating with cross-functional teams to refine data integration and iteratively improving the model based on stakeholder feedback. The outcome was a 15% improvement in prediction accuracy, significantly aiding our decision-making.”

2. Why do you want to join Meta?

Understanding why you want to join will help your interviewer determine if your values and aspirations align with Meta’s mission.

How to Answer

Your answer should reflect your understanding of Meta’s work, culture, and the specific opportunities that attract you to the company. Be honest and specific about how their offerings align with your career goals.

Example

“I want to join Meta because I am passionate about using data to solve meaningful problems that can impact millions of people worldwide. Meta’s mission to give people the power to build community aligns with my values. I see a unique opportunity to use my analytical skills to help enhance product features, to make a tangible difference in how people access and use information.”

3. Tell me about your data science background.

This question gauges the depth and breadth of your expertise. Knowing more about your professional accomplishments will also help the interviewers decide which team you’ll be best placed in.

How to Answer

Tailor your response to reflect the work you’re passionate about and have explored the most. Pick the top three or four achievements that best represent your journey. Meta values strong foundational concepts, so highlight any relevant education you have in the field.

Tip: For every point you include, ask yourself why it might be relevant in the interview. Link your answers to the role you’ll be expected to fill.

Example

“I’ve been working in data science for over five years, with a foundation in statistics from my undergraduate degree. My professional journey began as a data analyst, where I honed my SQL and data visualization skills, using user data insights to inform product enhancements. Progressing to a data scientist role, I led projects focused on predictive modeling and machine learning to optimize marketing campaigns. For instance, I developed a model that predicted customer lifetime value with high accuracy, which helped tailor the firm’s marketing strategies. At Meta, I’m excited to use my experience to further enhance user engagement through data-driven insights.”

4. How do you prioritize multiple deadlines?

In a global organization like Meta, you will work across teams, projects, and geographies. Time management and organization are essential skills for success.

How to Answer

Emphasize your ability to differentiate between urgent and important tasks. Mention any tools or frameworks you use for time management and showcase your ability to adjust priorities.

Example

“In a previous role, I often juggled multiple projects with tight deadlines. I prioritized tasks based on their impact and deadlines using a combination of the Eisenhower Matrix and Agile methodologies. I regularly reassessed priorities to accommodate changes and communicated proactively with stakeholders about progress and any potential delays.”

5. Tell me about a time when you had to take the lead in a challenging situation.

Even in non-leadership roles, leadership qualities are highly valued by employers because they give them an idea of whether you can take initiative, especially in a high-stakes situation.

How to Answer

Describe the context, the challenge, your response, and the outcome following the STAR framework. Highlight how you motivated the team and any critical decisions you made. This article has more managerial and leadership-focused behavioral questions.

Example

“In my previous role, when our team was facing a critical deadline for launching a new analytics dashboard, the project lead unexpectedly had to take leave. I decided to coordinate the project’s final stages. I began by reassessing our priorities and redistributing tasks based on team members’ workloads. To address morale and ensure everyone felt supported, I initiated daily check-ins as a space for the team to talk about concerns and progress updates. We successfully met the deadline, and the dashboard received positive feedback for its functionality and user interface.”

6. What are the Z and t-tests?

These tests are fundamental statistical tools used to determine if there are significant differences between groups. At Meta, you might use these tests in A/B testing scenarios or to assess the performance of campaigns.

How to Answer

Clearly define both tests and explain when each is appropriate to use. Highlight the differences between them, particularly in terms of sample size and population standard deviation knowledge.

Example

“The Z-test and t-test are both methods used in hypothesis testing to determine if there are significant differences between the means of two groups. A Z-test is typically used when the sample size is large (usually over 30) and the population variance is known. On the other hand, a t-test is used when the sample size is smaller and the population variance is unknown. It uses an estimated standard deviation and adjusts for the uncertainty by using the t-distribution, which is more spread out than the normal distribution.”

7. You are provided a table with user_ids and the dates the users visited our platform. Find the top 100 users with the longest continuous streak of visiting the platform as of yesterday.

This tests your ability to work with user engagement metrics while testing your SQL coding skills.

How to Answer

Discuss the use of SQL functions to handle date computations and aggregation. Mention functions like DATEDIFF to calculate differences between dates and RANK() to order users by their streak lengths. Emphasize the need for sorting data first and handling any edge cases.

Example

“I would ensure the data is filtered to include visits only up to yesterday. Then, I’d use the LAG function to get each user’s previous visit date, right after sorting the data by user_id and date. With this, I’d apply the DATEDIFF function to find the difference in days between consecutive visits for each user. A streak is counted when the difference is exactly one day. I would use a case statement to reset the streak count when the difference is not one day and accumulate the streak lengths using a SUM() over a window partitioned by user_id. The RANK() function would finally order users by their longest streak and select the top 100.”

8. Looking at our weekly metrics, you see a slow decrease in the average number of comments per user from January to March. Why might the average number of comments per user be decreasing, and what metrics would you look into?

For Meta, gaining a deeper understanding of engagement metrics is crucial for preemptive action to reverse negative trends and improve product health.

How to Answer

Ask clarifying questions, state your assumptions clearly, and cite ways to validate your hypotheses. Watch this video to learn more about how to approach such problems.

Example

“A decrease in the average number of comments could indicate several issues. First, it might suggest changes in the user base, such as an influx of new users who are less active or existing users becoming less engaged over time. To understand this, I would look into metrics like the new user acquisition rate versus user churn and engagement metrics for different user cohorts over time.

Another reason could be changes to the product or its environment, such as a recent update that made commenting less intuitive or necessary. Investigating the adoption and usage rates of recent features, along with user feedback from surveys or support tickets during the same period, could offer insights into product-related causes.

External factors, such as seasonal changes or shifts in work patterns due to holidays or global events, could also impact user engagement. Comparing the trend with the same period in previous years and examining broader industry or global trends might help identify these external influences.

Lastly, it’s crucial to consider the overall user experience and satisfaction, which could be affected by issues like increased bugs or performance problems. Metrics like load time, error rates, and support ticket volumes related to commenting features would be valuable to examine in this context.”

9. You are provided two tables. One is an attendance log for every student in a school district, and the other is a summary table with demographics for each student in the district. Which grade level had the largest drop in attendance between yesterday and today?

This question is designed to assess how well you integrate data, which is a common requirement for data scientists at Meta.

How to Answer

Highlight the importance of joining tables efficiently. Mention using window functions and simple aggregation functions to calculate differences in attendance. Briefly state any assumptions you’re making or any likely data quality issues; this shows you pay attention to detail.

Example

“I would first join the attendance log table with the demographics summary table using a common key, like student ID. I would then filter the attendance records for only the two relevant days. Using the GROUP BY clause, I’d aggregate attendance by grade level for each day and calculate the total attendance. To find the change in attendance, I would subtract yesterday’s attendance from today’s for each grade. The LEAD or LAG functions could be useful here to compare day-to-day changes directly in a single query. Finally, I’d use an ORDER BY clause on the difference to identify the grade with the largest drop.”

10. Determine whether adding a feature identical to Instagram Stories to Meta is a good idea.

This is a common analysis case question that gauges your understanding of user behavior, market trends, and cross-platform synergies, all of which are key to Meta’s strategy of integrating its apps.

How to Answer

The expectations for product sense questions are deliberately kept ambiguous, and this type of problem can be large in scope. Clarify expectations at the outset. If you’re looking for a comprehensive framework for tackling open-ended case study questions, read our article here.

Example

“I’d look at user engagement data from Instagram to understand why and how users interact with Stories. This includes demographic data to see which user segments are most engaged. I’d then assess Meta’s current user engagement and compare demographic and behavior overlaps. A/B testing would be critical here: deploying the feature to a small, diverse group of Meta users could provide initial data on engagement and acceptance. I would monitor metrics such as the increase in daily active users, time spent on the app, and interaction rates with the Stories feature. If the test shows positive results that align with our goals—like increased user engagement and content sharing—this would suggest that implementing Stories on Meta could complement the success seen on Instagram.”

11. How would you evaluate YouTube’s video recommendations?

Understanding how to measure the effectiveness of recommendation systems can provide insights into how well you would fine-tune similar systems at Meta.

How to Answer

Focus on key performance indicators (KPIs) that measure the effectiveness of recommendation systems. Stress the importance of A/B testing to compare the performance of different recommendation algorithms.

Example

“To evaluate YouTube’s video recommendations, I would first define relevant metrics that directly reflect user engagement and satisfaction. Key metrics would include click-through rate (CTR) on recommended videos, and average watch time, which indicates how engaging and well-targeted the recommendations are. I would also look at user retention metrics to see how the recommendation system affects long-term user engagement. A/B testing would be essential, where different recommendation algorithms are tested against control groups to compare performance. Gathering user feedback through surveys would also give us qualitative insights that could be used to further optimize the recommendation system.”

12. Say you are tasked with analyzing how well a model fits the data given. You want to determine a relationship between two variables. What is the downside of only using the R-squared value to do so?

Meta’s data scientists need to be adept at assessing the quality of their models, so your concepts in machine learning and statistical modeling will be thoroughly tested.

How to Answer

Before explaining your logic, ask clarifying questions like: What kind of model is it? How many variables are in the design matrix? Be clear on the exact context of the business problem before diving into the solution.

Example

“R-squared measures the proportion of variance in the dependent variable that is predictable from the independent variables. However, it doesn’t provide any insight into whether the independent variables are the correct ones for predicting the outcome. Moreover, R-squared always increases as more predictors are added to a model, regardless of whether those variables are meaningful.

Therefore, we’d need to complement R-squared with other metrics like adjusted R-squared, RMSE, or cross-validation results to get a more holistic view of model performance.”

13. What do you think the distribution of time spent per day on Meta looks like? What metrics would you use to describe that distribution?

For a platform like Meta, understanding the distribution of time users spend daily is crucial for assessing overall platform health.

How to Answer

Your answer should show that you are well-versed in Meta’s platform and user base.

Example

“The distribution of time spent per day on Meta is likely right-skewed, as most users might log in for shorter durations, while a smaller group of highly engaged users could spend significantly longer times on the platform. This distribution might also exhibit multimodality, reflecting different types of user engagement patterns, for example, casual browsers versus highly engaged participants.

I’d use mean and median to understand central tendencies, which can highlight the average time spent and the typical user behavior, respectively. Finding the standard deviation would help me assess the variability in time spent across users. Skewness and kurtosis would be important metrics as well to understand the shape of the distribution.”

14. Mark Zuckerburg calls you at 7 pm and says he needs to know exactly what percentage of Meta stories are fake news by tomorrow at 7 pm. How would you measure this, given the time constraint?

This question tests your scrappiness, i.e., how well you can perform given specific constraints and/or limited resources.

How to Answer

Describe a strategy to leverage systems already in place for detecting fake news, combined with random sampling and quick manual verification.

Example

“Given the timeframe, I would use Meta’s existing infrastructure for detecting fake news. This includes any machine learning models currently operational that automatically flag potential fake news stories based on source credibility and user flags. I would extract a random sample of today’s Meta Stories and run them through this automated system to quickly get an estimate of how many are flagged as fake.

I would also organize a rapid manual review, taking help from my team to verify a representative sample of the flagged stories to estimate the model’s false positive rate. Combining these insights, I’d calculate an adjusted percentage of stories likely to be fake. I would present these findings to Mark with a confidence interval.”

15. We are considering two different layouts for Instagram Stories. How would you determine which layout leads to higher user engagement?

You will need to thoroughly understand A/B tests and ways to interpret them, as Meta frequently tests designs to see which ones users engage with more.

How to Answer

Apart from the details of the experiment, you should mention the importance of setting the right metrics for user engagement, such as click-through rates or time spent on the page. Clarify the success criteria at the outset and start from there.

Example

“First, I would define ‘user engagement’ for the purpose of this test, and determine the success criteria. I’d also select the metrics to define the success of one layout over another, such as the number of stories posted, the number of stories viewed, and the average time spent on stories per session. I’d run an A/B test for a few weeks to account for variability in user behavior. After the testing period, I would use a t-test to compare the engagement metrics between the two groups.”

16. Given that X and Y are independent random variables with normal distributions, what is the mean and variance of the distribution of 2X−Y when the corresponding distributions are X∼N(3,4) and Y∼N(1,4)?

This question gauges your understanding of basic statistics.

How to Answer

To tackle such questions, clearly state your approach before diving into the solution.

Example

“The mean of 2X −Y would be 2∗3−1=5. The variance, since X and Y are independent, would be calculated as $2^2∗4+(−1)^2∗4=16+4=20$. So, the distribution of 2X − Y is N(5,20).”

17. Given the timestamps of logins, how many Meta users were active on a mobile phone all seven days in a week?

This query helps evaluate user engagement over a continuous period, which you’ll need in order to examine user behavior on Meta’s apps further.

How to Answer

Describe each step of the code: filtering, grouping, and counting unique user activities across a specific period. Mention SQL functions you’d use and any caveats you’d consider in the business context.

Example

“I would approach this by first filtering the login data for entries from mobile devices. I’d then narrow down the logins to the specific week of interest. In the next step, I’d group data by user ID and date, ensuring each date is distinct, and then count the number of unique dates of activity for each user. Using the GROUP BY clause, I would group the results by user ID, and with the HAVING clause, I would filter out only those users who have logged in on all seven days of the week.”

18. Given two sorted lists, write a function to merge them into one sorted list. What’s the time complexity?

This tests your ability to efficiently manipulate datasets. At Meta, you’ll need to consolidate data from different sources, like user feedback from various platforms, into a single, organized dataset for analysis.

How to Answer

Implement a two-pointer technique to iterate through both lists simultaneously, comparing elements and adding the smaller one to a new list until you’ve gone through both lists. This minimizes the time and space needed to achieve a fully merged list.

Example

“I’d initialize two pointers at the start of each list. Comparing the elements at these pointers, I’d then add the smaller of the two to a new list and advance the pointer. This process repeats until all elements from both lists are in the new list. If one list is finished first, I’d append the rest of the other list directly. This method ensures a sorted merge and operates with a time complexity of O(n + m), where n and m are the lengths of the two lists.”

19. What is a recall metric?

Understanding the recall metric is essential in contexts like content moderation or spam detection, as regulating harmful content is critical to Meta’s success.

How to Answer

Discuss why recall is important and in what scenarios it is crucial to prioritize it over other metrics.

Example

“Recall is a performance metric used to evaluate the ability of a classification model to identify all relevant instances in a dataset. It is calculated by dividing the number of true positives by the sum of the true positives and false negatives. In simpler terms, it measures the proportion of actual positives that are correctly identified by the model.

For example, if we’re using a model to flag posts that violate community standards (where such posts are the ‘positives’), the recall would tell us what proportion of all violating posts we successfully flagged. A high recall rate means that the model is effective at catching most of the violations, which is crucial for maintaining the integrity of the platform.

Recall becomes a priority metric in scenarios where missing a positive case (such as not detecting a harmful post) has serious implications. However, focusing solely on recall can lead to a high number of false positives—where non-violating posts are incorrectly flagged as violations. Therefore, it’s important to balance recall with other metrics like precision, which measures the accuracy of the positive predictions made by the model.”

20. Let’s say you’re working with survey data sent as multiple-choice questions. How would you test if survey responses were randomly filled out by individuals or if the answers were truthful?

In a Meta context, ensuring the integrity of survey data would positively impact strategic decision-making, resulting in enhanced user experiences.

How to Answer

Discuss the statistical tests you would use to compare survey data against a uniform distribution expected from random selections. Mention the importance of understanding the survey’s context and expected response patterns to select the appropriate test.

Example

“I would first analyze the distribution of answers for each multiple-choice question. If responses were random, we’d expect a relatively uniform distribution across all options. To test this, I would use a chi-square test comparing the observed distribution of responses to the expected uniform distribution. Additionally, analyzing the pattern of responses across questions for each respondent might reveal anomalies, such as selecting the same option for all questions.”

21. Given a sorted list of positive integers with some entries being None, write a function to return a new list where all None values are replaced with the most recent non-None value in the list.

This question might be asked to assess your problem-solving skills and ability to work with incomplete or irregular data, which is common in real-world scenarios. It tests your understanding of list manipulation, iteration, and handling edge cases.

How to Answer

When answering this question, focus on explaining your approach step by step. Start by mentioning that you need to handle missing data, and ensure each entry is replaced with the most recent valid value. Describe how you would iterate through the list, checking each entry and replacing None values as needed.

Example

“In tackling this problem, I would start by considering how to handle the missing data represented by None values. I could initialize a variable to store the most recent valid value, starting with 0 if the first entry is None. As I iterate through the list, I would check each entry, replacing any None values with the last stored value. This approach should ensure that the list remains consistent and accurately reflects the intended sequence, even with gaps in the data.”

22. Where and how could you promote Instagram through Meta (Facebook)?

This question would likely be asked in a Meta Data Scientist interview to assess your ability to think strategically about cross-platform promotion and user engagement. Meta, being a company with multiple interconnected platforms, values candidates who can identify opportunities for growth and can leverage one platform to drive traffic to another.

How to Answer

This product question is more focused on growth and is very much used for Facebook’s growth marketing analyst technical screen. Here are a couple of things that we have to remember.

Like usual product questions where we are analyzing a problem and coming up with a solution with data, we have to do the same with growth except we have to come up with solutions in the form of growth ideas and provide data points for how they might support our hypothesis.

Example

“I would promote Instagram on Facebook by leveraging network effects, such as notifying users when their friends join Instagram and encouraging them to do the same. This could be tested through an A/B test to see if notifications drive higher sign-up rates. Additionally, allowing Instagram posts to be shared on Facebook could create engagement that leads users back to Instagram. By focusing on strategies that use existing social connections, I believe we could effectively drive growth for Instagram through targeted promotions on Facebook.”

Here are some tips to help you excel in your Meta interview.

Tailor Your Resume

Understand the job description clearly and prepare your resume accordingly. The resume screening determines whether you’ll make it to the interview process, so highlight your work experience and skills the recruiter wants to see.

Study the Company and Role

Research recent news, updates, Meta values, and business challenges the company is facing. Understanding the company’s culture and strategic goals will allow you to better present yourself and know if they are a good fit for you.

Meta has an excellent resource for preparing for data science interviews, so read this whitepaper to better prepare for the interview.

Further, if you know data scientists or product managers who work at Meta, it’s a good idea to talk to them to understand what will be expected of you. If possible, leverage your LinkedIn network to chat with Meta employees or ex-employees.

Understand the Fundamentals

Brush up on topics like statistics, probability, product sense, and model evaluation. Be comfortable with Python, SQL, and the Python libraries commonly used for statistical modeling, like pandas, scikit-learn, and TensorFlow. Here are some resources we’ve compiled for SQL, Python, and other categories of data science interview questions.

For further practice, refer to our popular guide on quantitative interview questions.

If you need additional guidance, we also offer our tailored data science learning path covering core topics and practical applications.

Highlight Your Soft Skills

Soft skills such as collaboration, effective communication, and flexibility are paramount to succeeding in any job, especially in a dynamic work environment such as Meta. Here is a resource we’ve compiled on top behavioral questions.

To test your current preparedness for the interview process, try a mock interview to improve your communication skills. It is particularly useful to have multiple mock interviews for case study rounds.

What is the average salary for a data science role at Meta?

The average base salary for a data scientist at Meta is US$160,099, one of the highest in the US for data scientists. It is well above the average data scientist base compensation, which is US$123,030.

What other companies can I apply for besides Meta’s data scientist role?

You can apply to similar roles in other MAANG companies. We have interview guides for Google, Apple, Amazon, and Netflix.

For insights on other tech jobs, you can read more on our Company Interview Guides page.

Are there job postings for Meta data science roles on Interview Query?

Yes, several such roles are open on our job portal. There, you can search by location, team, and skill sets and apply for your desired role. We also have posted several data science job openings for other firms.

Succeeding in a Meta data science interview requires a strong understanding of the business and its products, fundamental statistical knowledge, and the ability to creatively apply your technical skills to solve the company’s challenges.

Understanding Meta’s experimentation-driven culture and preparing thoroughly with both technical and behavioral questions will be critical for success. For other data-related roles at Meta, consider exploring our guides for data analyst, data engineer, ML engineer, and other positions in our main Meta interview guide.

We wish you the best in your journey to landing a fulfilling role at Meta.