Cracking the Meta Data Science Interview: 20 Questions from 2024

Meta offers an exceptional environment for Product Data Scientists. While I, Nick Singh, may be biased as a former member of Facebook's Growth Team and co-author of a book with my friend Kevin Huo, a Data Scientist at Facebook, our admiration for the company is genuine. We're eager to assist you in securing a position there! In this guide, we'll provide an insider's perspective on the Meta Product Analytics Data Science interview process and discuss recent Meta Data Science interview questions.

Deciphering the Meta Product Analytics Data Science Interview Process

The interview process at Meta typically extends over 4 to 6 weeks and comprises multiple rounds focusing on SQL skills, product understanding, and analytical case studies. Here's a breakdown of each stage:

Round 1: Recruiter Screening

Your first step in the Meta interview process involves a recruiter screen:

• Style: Phone call
• Time: 30-45 minutes
• Interviewer: Technical Recruiter or Talent Acquisition Specialist
• Subject Matter: Culture fit, experience overview, logistical details

Insider Tip: Craft a compelling response to the question, "Why do you want to be a Product Data Scientist at Meta?" Share an experience where you collaborated closely with business and product stakeholders, incorporating key terms like A/B testing, product analytics, and SQL, as these are sought-after skills by Meta recruiters.

Important Note: Product Analytics Data Scientists at Meta primarily focus on SQL and product sense, rather than building machine learning models. Avoid overemphasizing topics like deep learning or PyTorch, as they may not align with the role requirements.

Round 2: Technical Screening

Following the recruiter screening, you'll advance to a virtual technical screening:

• Style: Virtual video call
• Time: 45-60 minutes
• Interviewer: Hiring Manager or Senior Data Scientist
• Subject Matter: Assessing SQL skills, product case studies The technical screening often involves an SQL test using platforms like Coderpad, where the interviewer observes your coding in real-time.

Insider Tip: Precision and efficiency with SQL are key at Meta. If you're rusty due to primarily using R or Python, practice beforehand. Meta utilizes the SQL screen as a straightforward filter, so strive for accurate SQL coding during this round.

To prepare, and tackle actual SQL interview questions asked by Meta in the past. We've compiled a list of these questions in our article, 9 Meta/Facebook SQL Interview Questions, along with an interactive coding pad to facilitate practice.

Last Round: 4-5 Virtual Interviews

You'll discover your progression to the final round within one to three weeks after your technical assessment. The last phase of the Meta Data Science interview usually encompasses four 45-minute virtual sessions, with each delving into a distinct subject:

• Style: Virtual video call
• Time: Each segment spans 45 minutes
• Interviewer: Hiring Manager or Senior Data Scientist
• Subject Matter: Product case studies, metric definitions, statistics and A/B testing, SQL, and behavioral inquiries

Meta Data Science Interview Queries

The Data Science interviews at Meta are renowned for their rigorous and varied question styles, evaluating a broad spectrum of abilities from technical proficiency to business insight. In this segment, we provide sample queries from each category that were asked by Meta this year!

Meta Product Metrics Questions

1. How do you quantify the impact of Meta's news feed algorithm updates on user engagement metrics, such as daily active users and time spent on the platform, over the last quarter?
2. What metric would you suggest to gauge the effectiveness of Meta's new "Events" feature in enhancing user interaction and event participation on the platform?
3. Can you design a metric to measure the influence of Meta's advertising campaigns on user retention and engagement, considering factors like ad impressions, click-through rates, and user interactions?

Meta Analytics Execution Questions

1. How would you design an experiment to analyze the impact of Meta's algorithm changes on user engagement metrics, ensuring statistical validity and minimizing biases?
2. Can you outline a process for assessing the quality and reliability of data collected from various sources for ad targeting on Meta's platform, and propose measures to address any identified issues?
3. Suppose Meta introduces a new feature to enhance content recommendation accuracy. How would you monitor and evaluate the performance of this feature over time, identifying any potential issues or areas for improvement?

Meta Analytical Reasoning Questions

1. If Meta observes a decline in user engagement metrics following a platform update, how would you analyze the data to identify potential causes and recommend strategies for improvement?
2. Suppose Meta wants to understand the factors influencing user adoption of a new feature. How would you conduct a regression analysis to identify significant predictors of feature adoption and provide insights to inform product development decisions?
3. Can you analyze user behavior data to identify patterns and trends in user engagement with Meta's video content, and propose recommendations to optimize content delivery and enhance user experience?
4. Suppose Meta wants to expand its user base in a specific demographic segment. How would you use data analysis techniques to identify characteristics and preferences of the target demographic and develop targeted marketing strategies to attract and retain users within this segment?

Meta A/B Testing & Research Design Interview Questions

1. Suppose Meta wants to test two different versions of its news feed algorithm to determine which one leads to higher user engagement. How would you design an A/B test to compare the performance of the two algorithms and ensure reliable results?
2. Can you outline a research design to evaluate the effectiveness of a new feature aimed at increasing user interaction with events on Meta's platform, including key metrics to measure and methods to control for confounding variables?
3. Suppose Meta is considering changes to its advertising placement strategy to improve click-through rates. How would you design an A/B test to compare the performance of the current and proposed placement strategies and determine which one yields better outcomes for advertisers?

Meta SQL Questions

1. Given a dataset containing user interactions on Meta's platform, including columns for user_id, timestamp, and action_type (e.g., "like", "comment", "share"), write an SQL query to calculate the total number of interactions per user within the past week.
2. Suppose Meta tracks revenue from advertising campaigns in a table named ad_revenue, with columns for campaign_id, date, and revenue_amount. Write an SQL query to calculate the total revenue generated from all campaigns in the last month.
3. Consider two tables: posts and comments. The posts table contains columns for post_id and creation_date, while the comments table contains columns for comment_id, post_id, and creation_date. Write an SQL query to find the number of posts that received at least 10 comments within the last month.
4. Given a table named user_activity with columns for user_id, date, and activity_type (e.g., "login", "post", "comment"), write an SQL query to calculate the daily active users (DAU) for the past week, defined as the count of unique users who performed any activity on each day.

Meta Behavioral Questions

1. Can you describe a situation where you had to collaborate with cross-functional teams to solve a complex problem while working at Meta? How did you approach the collaboration, and what was the outcome?
2. Share an experience where you had to adapt to unexpected changes or challenges while working on a project at Meta. How did you handle the situation, and what did you learn from it?
3. Can you recall a time when you had to manage competing priorities or tight deadlines while working on multiple projects at Meta? How did you prioritize tasks and ensure timely delivery?

Best Resources to Prepare for the Meta Data Science Interview

1. DataLemur: 200+ SQL interview questions from Meta, and other big-tech companies like Amazon, Google, TikTok, Netflix etc.
2. Meta Careers Website: Visit this site to learn more about the company culture, values, and available data science roles.
3. Ace the Data Science Interview: written by 2 Ex-Facebook employees, this is the go-to resource for Acing the Meta Data Science Interview. The book has 201 real FAANG interview questions, including 11 from Facebook/Meta.
4. Meta Data Science Interview Guide: this guide gives you 30 more Meta interview questions and tips to help you prepare!
5. Glassdoor Meta Data Science Interview Reviews: Provides interview reviews from past candidates, including insights into the interview process, questions asked, and tips for preparation.