Unlocking the Meta Data Science Interview: 18 Questions from 2024
DataLemur ?? (Ace the SQL & Data Interview)
Practice 200+ FAANG SQL & Data Interview questions! Made by Nick Singh (Ex-FB & Author of Ace the Data Interview ??)
Meta is an amazing place to be a Product Data Scientist. I Nick Singh ???? might be a bit partial, though— I was on Facebook's Growth Team and co-authored a book with my close friend Kevin Huo who was a Data Scientist at Facebook. We both admire the company greatly, and we'd love to help you land a job there too!
In this guide, we're going to give you an inside look at the Meta Product Analytics Data Science interview process and share some recently asked Meta Data Science interview questions.
Understanding the Meta Product Analytics Data Science Interview Process
The interview process at Meta typically spans 4 to 6 weeks and includes multiple rounds focusing on SQL skills, product sense, and analytical case studies. Here's a breakdown of what to expect in each stage:
Round 1: Recruiter Screening
Your first step in the Meta interview process is the recruiter screen:
- Format: Phone call
- Duration: 30-45 minutes
- Interviewer: Technical Recruiter or Talent Acquisition Specialist
- Questions: Culture fit, experience overview, and logistical details
Insider Tip: Prepare a compelling response to the common question, "Why do you want to be a Product Data Scientist at Meta?" A good approach is to recount an experience where you worked with data and collaborated closely with business and product stakeholders. Incorporate key terms like A/B testing, product analytics, and SQL, as these are the skills Meta recruiters look for.
Important: Product Analytics Data Scientists at Meta do not build machine learning models. If you focus too much on deep learning, PyTorch, or similar topics, it could indicate a misalignment with the role, which is more centered around SQL and product sense.
Round 2: Technical Screening
After the recruiter screening, you'll move on to a virtual technical screening:
- Format: Virtual video call
- Duration: 45-60 minutes
- Interviewer: Hiring Manager or Senior Data Scientist
- Questions: SQL skills, product case studies
The technical screening typically involves an SQL test using Coderpad or a similar platform where the interviewer observes you writing code in real time.
Insider Tip: At Meta, speed and accuracy with SQL are crucial. If you're rusty because you typically use R or Python, you need to practice beforehand. Meta uses the SQL screen as a straightforward filter to eliminate candidates, so aim for precise and efficient SQL coding during this round.
The most effective way to prepare for the technical screen is to work on actual SQL interview questions that Meta has asked in the past. We've compiled a list of these questions in our article, 9 Meta/Facebook SQL Interview Questions , and created an interactive coding pad to help you practice.
Final Round: 4-5 On-Site Interviews
You'll find out if you've advanced to the final round one to three weeks after your technical screening. The on-site Meta Data Science interview typically consists of four 45-minute interviews, each focusing on a different topic:
- Format: Virtual video call
- Duration: Each session lasts 45 minutes
- Interviewer: Hiring Manager or Senior Data Scientist
- Topics: Product case studies, metric definitions, statistics and A/B testing, SQL, and behavioral questions
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Meta Data Science Interview Questions
Meta's Data Science interviews are known for their rigorous and diverse question types, assessing a wide range of skills from technical expertise to business acumen. In this section, give you sample questions from each type that were asked by Meta this year!
Meta Product Metrics Questions
1. Define the Key Metrics for a New Product: Imagine Meta is launching Facebook Dating. What key metrics would you track to determine the feature's success, and why?
2. Diagnose a Drop in Engagement: If you noticed a significant drop in user engagement for a specific product within Meta, what steps would you take to investigate the cause, and which metrics would you analyze to understand the issue?
3. Prioritize Metrics for Product Improvements: When considering improvements to an existing product at Meta, which metrics would you prioritize to guide your decision-making, and how would you use those metrics to drive meaningful changes?
Meta Analytics Execution Questions
1. Evaluating Experiment Results: Given a dataset from an A/B test for a new Meta feature, describe the steps you would take to analyze the results and determine whether the experiment was successful.
2. Data Quality Assessment: If you were given a data source to use for a critical analytics project at Meta, what checks would you perform to ensure the data's quality and reliability before beginning your analysis?
3. Identifying Trends and Insights: How would you approach analyzing a large dataset to identify meaningful trends and insights? Describe the tools and techniques you would use, and how you would validate your findings.
Meta Analytical Reasoning Questions
1. Assessing a Product Change: Suppose Meta decided to make a significant change to its user interface for Facebook Marketplace, where they added more granular product filters. What analytical approach would you use to evaluate the impact of this change? What data would you need, and how would you measure success?
2. Analyzing User Behavior: If you noticed an unexpected shift in user behavior on a Meta platform, what steps would you take to understand the root cause? Explain your analytical process and which data points would be most important to examine.
3. Optimizing Product Performance: If a Meta product's key performance metrics showed a downward trend, how would you diagnose the problem and recommend solutions? Describe the analytical techniques and data sources you would rely on to guide your investigation and conclusions.
Meta A/B Testing & Research Design Interview Questions
1. Designing an A/B Test: Suppose Meta wants to test a new feature on its platform. Describe how you would design an A/B test to evaluate the feature's effectiveness. What variables would you control for, and how would you determine the appropriate sample size?
2. Interpreting A/B Test Results: If you were presented with the results of an A/B test showing that the new feature improved user engagement, how would you determine if the results are statistically significant? What additional checks would you perform to ensure the validity of the results?
3. Dealing with Confounding Variables: In an A/B test, how would you account for potential confounding variables that could influence the results? Provide an example of a confounding variable in an A/B testing context and explain how you would address it in your research design.
Meta SQL Questions
1. Basic SQL Query: Given a table with user data containing columns like user_id, signup_date, and country, write an SQL query to find the number of users who signed up in 2023 from the United States.
2. Complex SQL Query with Joins: Consider two tables: users with columns user_id and country, and transactions with columns transaction_id, user_id, and amount. Write an SQL query to find the total transaction amount for each country.
3. SQL with Aggregation: Given a table page_views with columns user_id, page_id, and timestamp, write an SQL query to find the top 3 pages with the most views in the past 7 days. Include the count of views for each of these top pages.
Meta Behavioral Questions
1. Handling Conflict in a Team: Describe a situation where you disagreed with a colleague or team member. How did you handle the conflict, and what was the outcome? What did you learn from the experience?
2. Dealing with Ambiguity: Can you share an example of a project where you had to work with incomplete or ambiguous information? How did you manage the uncertainty, and what steps did you take to ensure a successful outcome?
3. Leading a Project: Tell me about a time when you led a project or initiative. What challenges did you face, and how did you motivate your team to achieve the desired results? What leadership skills did you use to guide your team?
Best Resources to Prepare for the Meta Data Science Interview
Loving this! It’s the main reason why we have built the complete list, recruiter-vetted, behavioral interview questions deck, including questions, frameworks to answer them like STAR as well as example answers https://9to5cards.com/product/the-behavioral-interview-deck/
Great Meta tips here!
Founder: DataLemur.com (SQL Interview Prep) ? Author: Ace the Data Science Interview ? Ex-Facebook ? 160K+ follow me on LinkedIn for Data/SQL/Career tips!
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