Decoding the Meta Data Science Interview: 21 Questions from 2024

Updated on 
June 17, 2024
by Nick Singh
By Nick Singh
created at : 

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Meta offers an incredible environment for Product Data Scientists. As someone who was part of Facebook's Growth Team and collaborated on a book with my friend Kevin Huo, a former Data Scientist at Facebook, I may be a bit biased. Nonetheless, our admiration for the company is genuine, and 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 some of the latest Meta Data Science interview questions.

Navigating the Meta Product Analytics Data Science Interview Process

The Meta interview process usually takes about 4 to 6 weeks and has a few rounds focusing on SQL skills, understanding products, and solving analytical problems. Here's what each step looks like:

Round 1: Chat with the Recruiter

First off, you'll have a chat with a recruiter:

  • How it Goes: Phone call.
  • How Long: 30-45 minutes.
  • Who's Asking: Technical Recruiter or Talent Acquisition Specialist.
  • What They Ask: They'll want to know if you fit in with the company culture, hear about your experience, and sort out some logistics.

Tip: Be ready to answer why you're keen on being a Product Data Scientist at Meta. Share a story about working with data and teaming up with folks in business and product roles. Sprinkle in words like A/B testing, product analytics, and SQL—those are the skills they're after.

Key Reminder: At Meta, they're not big on machine learning for this role. So if you go on about deep learning or PyTorch too much, it might not vibe with what they're looking for, which is more about SQL and understanding products.

Round 2: Tech Check

After the recruiter chat, you'll dive into a tech assessment:

  • How it Works: Video call.
  • How Long: 45-60 minutes.
  • Who's on the Other Side: Hiring Manager or Senior Data Scientist.
  • What's on the Table: They'll throw some SQL tasks and product case studies your way.

During this part, you might have to tackle an SQL test using something like Coderpad, where they can watch you code as you go.

Tip: Speed and accuracy with SQL are key. If you're more used to R or Python and a bit rusty with SQL, it's time to brush up. Meta uses this as a clear-cut way to sift through candidates, so aim for smooth sailing here.

For practice, hit up real SQL questions from Meta's past interviews. Check out our article, 9 Meta/Facebook SQL Interview Questions, where we've got a bunch of them laid out for you, alongside a tool to help you practice.

Final Showdown: 4-5 Last-Chance Chats

After your tech check, you'll get the verdict on whether you're moving forward, usually within one to three weeks. If you're in, get ready for the final round of Meta Data Science interviews—it's a series of four 45-minute sessions, each diving into a different area:

  • How It Works: Video Call.
  • How Long: 45 minutes each.
  • Who's on the Other Side: A mix of Hiring Managers and Senior Data Scientists.
  • What's on the Table: Expect product case studies, talk metrics, crunch stats, throw SQL tasks your way, and maybe even slip in a few behavioral questions.

It's your chance to shine, so be ready to confidently tackle each topic!

Meta Data Science Interview Questions

Meta's Data Science interviews are famed for their challenging and varied questions, evaluating a broad spectrum of abilities spanning technical prowess to business insight. In this section, we provide sample questions from each category that were posed by Meta this year!

Meta Product Metrics Questions

  1. User Engagement Measurement: How would you define and measure user engagement metrics for Meta's news feed feature, considering factors like time spent on the platform, frequency of interactions, and content consumption patterns?
  2. Retention Rate Analysis: Describe the key metrics you would use to assess user retention for Meta's messaging service over a six-month period. How would you interpret fluctuations in retention rates and identify potential factors influencing user retention?
  3. Revenue Generation Evaluation: Suppose Meta introduces a new advertising feature. What metrics would you track to evaluate the effectiveness of this feature in generating revenue for the platform? How would you quantify the impact on advertiser engagement and revenue growth?
  4. Content Performance Assessment: How would you measure the success of user-generated content on Meta's platform, such as posts, photos, and videos? What metrics would you use to assess content virality, user engagement, and overall platform activity related to user-generated content?

Meta Analytics Execution Questions

  1. Experimental Design Evaluation: If Meta plans to launch a new feature allowing users to customize their profile pages, how would you design an experiment to measure the impact of this feature on user engagement? Describe the key elements of your experimental design, including sample size determination, randomization, and control group selection.
  2. Data Quality Assessment: Imagine Meta collects user data from various sources for ad targeting purposes. How would you assess the quality and reliability of this data before using it for analytics? Outline the steps you would take to identify and address potential issues such as data completeness, accuracy, and consistency.
  3. Performance Monitoring: Meta introduces a new algorithm to improve content recommendation accuracy on its platform. How would you monitor the performance of this algorithm over time? Describe the metrics you would track to evaluate the algorithm's effectiveness, and outline your approach to detecting and addressing any performance degradation or bias issues.
  4. Insights Generation from User Behavior Data: Given a dataset containing user interactions with Meta's video content, how would you analyze this data to identify trends and insights? Describe the analytical techniques you would use to uncover patterns in user behavior, such as video viewing preferences, engagement levels, and content consumption habits.

Meta Analytical Reasoning Questions

  1. Impact Analysis of Platform Changes: Suppose Meta updates its news feed algorithm to prioritize content from friends and family over news articles. How would you analyze user engagement metrics before and after the algorithm change to assess its impact on user experience and platform usage? What additional data or analyses would you consider to understand the implications of this update?
  2. Market Segmentation Strategy: Meta is planning to expand its advertising services to target small businesses. How would you use data analytics to segment the market and identify potential target audiences among small businesses? Describe the analytical approach you would take to identify relevant characteristics and preferences of different market segments, and how you would use this information to tailor advertising strategies.
  3. Competitive Analysis: If Meta's competitor launches a new feature similar to Meta's existing product, how would you analyze user feedback and adoption rates to evaluate the competitive threat? Describe the metrics and analytical techniques you would use to compare the performance of the competitor's feature with Meta's offering, and how you would use this analysis to inform product strategy decisions.

Meta A/B Testing & Research Design Questions

  1. Optimizing News Feed Algorithm: Meta is considering two variations of its news feed algorithm to increase user engagement. How would you design an A/B test to compare the performance of the current algorithm with the proposed variations? Describe the experimental design, including hypothesis formulation, randomization, and sample size calculation, to ensure statistically valid results.
  2. Assessing Ad Placement Effectiveness: Meta wants to test different placements for ads within its mobile app to maximize click-through rates. How would you design an A/B test to compare the effectiveness of different ad placements? Outline the key elements of the experimental setup, including control group selection, measurement metrics, and duration of the test, to accurately evaluate the impact of ad placement on user engagement.
  3. Testing Feature Adoption Strategies: Suppose Meta introduces a new feature aimed at increasing user interaction with events on its platform. How would you design an A/B test to assess the effectiveness of different strategies for promoting feature adoption among users? Describe the experimental design, including treatment allocation, measurement criteria, and analysis methods, to identify the most successful approach for driving user engagement with the new feature.

Meta SQL Questions

  1. User Activity Analysis: 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. Revenue Calculation: 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. Content Performance Comparison: 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. User Engagement Trends: 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. Collaboration Scenario: Describe a time when you had to collaborate with a cross-functional team to solve a complex problem. What was your role in the team, and how did you ensure effective communication and collaboration among team members? What was the outcome of the project, and what did you learn from the experience?
  2. Handling Pressure: Can you recall a situation where you had to meet tight deadlines or handle a high-pressure work environment? How did you manage your time and prioritize tasks to meet the deadline? Did you encounter any challenges, and how did you overcome them?
  3. Adapting to Change: Share an example of a time when you had to adapt to unexpected changes or challenges in a project or work environment. How did you approach the situation, and what steps did you take to adjust your plans or strategies accordingly? What was the result, and what did you learn from the experience?

Best Resources to Prepare for the Meta Data Science Interview

Looking for more resources? Well look no further!

  1. DataLemur: 200+ SQL interview questions from Meta, and other big-tech companies like Amazon, Google, TikTok, Netflix etc.
  2. Cracking the PM Interview by Gayle Laakman McDowell: good for the Meta Product-Sense & Product Case Study Questions.
  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. Meta Careers Website: Visit this site to learn more about the company culture, values, and available data science roles.

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