Meta Data Science Manager Interview

Interview Guide Apr 09

The role of a Meta Data Science Manager

The Data Science Manager role at Meta is all about leveraging your skills in people leadership, project management, analytics, and tech—so you can contribute to the development of experiences for billions of users and millions of businesses. These positions typically involve leveraging SQL for data querying and manipulation tasks, as well as conducting product case studies to derive insights and make data-driven decisions.

Meta emphasises data-driven decision-making, so you'll play a key role in shaping product strategy. You'll work with various teams across different functions, influencing product strategy with the power of data.

The team culture at Meta is top-notch, and the opportunities for skill development and career growth for DS managers are substantial. 

Plus, the remuneration for a DS Manager at Meta is pretty attractive. In total, the yearly compensation amounts to $260,000—with a solid base salary, stock options, and a bonus. It's an exciting role for anyone looking to make a dent in the world of data science at a global scale.

Meta Data Science Manager Interview Guide

The interview process for a Data Science Manager is not unlike that for the data scientist role, but has a major focus on managerial aspects. It involves four main rounds, namely:

  1. Recruiter Call
  2. Personal Call with HR
  3. Technical interview
  4. On-site Interview

Recruiter Call


This initial conversation with the recruiter serves as an introduction to the role and the interview process. The recruiter will provide you with more information about the position, including responsibilities, team dynamics, and expectations. They may also ask about your background and experience to ensure it aligns with the requirements of the role.

Personal HR Screen


This is a one-on-one call with the hiring manager, where you'll be asked about your past experiences, projects you've worked on, your leadership style, and so on. They will also dig deeper into your understanding of Meta's culture, why you wish to join the DS management field, and how you envision contributing to it.

Technical Screen


This part of the interview is quite intensive and typically involves a SQL case study question. You'll need to propose a metric, write SQL code to calculate it, and then use that metric to investigate a problem. Questions in this round often have multiple parts and focus on SQL proficiency and product case studies.

Onsite Round


If you make it past the first screening, you'll be asked to come in for an onsite interview. During this interview, you'll have five one-on-one rounds. Typically, there is only one technical round, and four rounds that are behavioural (2) and leadership (2) in nature.

While technical questions covering areas like SQL, Python, and machine learning are also included during data science management interviews at Meta, the primary focus is on discussion-based scenarios that evaluate your leadership abilities, execution skills, and team management capabilities.

Here's what to expect in the onsite interview round:

Technical Round: Unlike traditional technical interviews where coding might be the focus, the technical round for manager roles often takes the form of case study questions. These cases could revolve around real-world scenarios in the realm of data science (SQL, Python, and machine learning) — such as product development challenges or machine learning applications. While you won't need to write code, you'll need to showcase your analytical abilities, critical thinking skills, and strategic mindset in approaching these cases.

Leadership Rounds: You can anticipate two rounds dedicated to assessing your management style, your approach to leading and inspiring teams, and your effectiveness in communication. You'll likely engage in discussion-based questions, for instance, "Can you share an example of how you motivated a team to meet tight deadlines?" and "How would you resolve conflicts within a team?" You'll need to share examples of your leadership experiences so as to provide insights into how you handle tricky scenarios. 

People Management Questions: A significant portion of the interview will focus on your people management skills. This could include questions that require you to share examples of how you identify and recruit top talent, how you mentor and develop team members, or how you handle conflicts or performance issues. For instance, "Describe a time when you recruited top talent for your team" or "How did you handle a challenging team dynamic?"

Product Leadership Questions: In this part of the interview, expect questions that delve into your ability to lead and drive impact with data science initiatives across the organisation. You may be asked about your experience in setting goals, measuring success, managing projects, and maximising the output of a data science team. So, stuff like "Can you provide an example of how you collaborated with cross-functional teams to implement data-driven solutions?" and "How have you gained buy-in for data science projects from stakeholders across different departments?”

Business/Product Sense Questions: Here, you'll face general product sense questions about how data science affects a business. So, expect questions like, "How can data science help a business?" or "Can you give examples of how data can improve a company's operations?” These are more about showing your smarts and instincts than about how you manage things. They want to see how well you grasp the big picture of data's role in business success.

Behavioural Rounds: You can expect a strong emphasis on behavioural questions, particularly those related to leadership, problem-solving, product management, and team management. . So, you will be asked to share examples of projects where you delivered impactful results using data-driven insights. Expect questions such as "Can you share an example of a project where you delivered impactful results using data-driven insights?" Or "Describe a time when you successfully managed a project through its entire lifecycle, overcoming any obstacles along the way."

Interview Questions

Technical Questions 

  • Design a system to identify bots on Facebook.
  • Given a table of posts and a table of reactions to posts, calculate the average number of likes per post over the past 28 days.
  • Solve basic SQL manipulation questions and discuss product management scenarios.
  • Explain the concepts of Recall and Precision.
  • Write a query to count the number of entries per category based on a table definition.
  • Write a query to find the maximum number of accepted friendship requests by a user within a specific timeframe, based on table definitions.

Leadership/People Management Questions:

  • How do you approach the hiring process, and what strategies do you employ for retaining talent?
  • What is your experience with cross-functional collaboration, and how do you typically engage with it?
  • How do you garner support and buy-in for your projects?
  • How would you ensure that your department's goals align with the broader objectives of the company?
  • What methods do you use to motivate and inspire your team, and how do you handle conflicts within the team?
  • How do you address instances of low performance?
  • What approaches do you take when resolving conflicts within your team?

Product Leadership/Product Sense Questions:

  • How might you assess if Spotify should venture into the podcasting sector?
  • How would you gauge the effectiveness of YouTube Shorts?
  • If you were to suggest a fresh aspect for DoorDash, what might it be and why?
  • What method would you employ to expand a team of content reviewers for data labelling purposes?
  • Share an example of a goal you achieved using data science.
  • Given a business case about how you would increase engagement in a mobile app, propose a set of KPIs to monitor and what data to use?
  • How would you go about expanding data science capabilities across a company?
  • What criteria do you consider when selecting Key Performance Indicators (KPIs) for a product or team? What information is essential, and with whom would you need to collaborate?
  • What strategies do you employ to enhance a team's productivity?

Behavioural Questions:

  • Explain how you communicate complex technical concepts to non-technical audiences.
  • Describe your approach to gaining support for data science projects.
  • Recount a situation when you successfully achieved a goal despite limited resources.
  • Outline your strategy for meeting tight deadlines. What actions do you take when faced with an apparently unattainable deadline?
  • Reflect on a time when you experienced failure. What were the circumstances, and what did you learn from it?
  • Discuss your approach to resolving conflicts in the workplace.
  • Have you ever handled sensitive information in your work? Describe your approach.
  • How do you cultivate relationships with colleagues and earn their trust?

Meta Data Science Manager Roles and Responsibilities

Following are the roles and responsibilities of a Meta Data Science Manager:

  • Your main responsibilities include leading a team of data scientists to develop strategies for global products. This involves working closely with Product, Engineering, and other teams to manage analytics projects from start to finish and influence product strategies and investments.
  • You'll need to analyse complex datasets using different statistical methods to solve tough problems. 
  • You'll identify and test opportunities for product enhancement, shaping roadmaps based on insights and recommendations.
  • Your role also entails promoting data best practices, improving analytical processes, scaling tools and knowledge, and mentoring other data scientists.

Meta Data Science Manager Skills and Qualifications

Here are the skills and qualifications that a Meta Data Science Manager must have:

  • A Bachelor's degree is a must, but having a Master's or Ph.D. in fields like Mathematics, Statistics, Computer Science, Engineering, or Economics is highly beneficial.
  • You should have at least 4 years of experience in applied analytics. If you have a Ph.D., 2+ years of experience is okay, with at least 2 years spent managing analytics teams.
  • You must be skilled in data querying languages like SQL, scripting with Python, and using statistical/mathematical software like R.
  • You should have a proven track record of leading analytical projects independently, demonstrating leadership and the ability to work without constant supervision.
  • You should be able to clearly and concisely explain complex findings to senior leaders to aid decision-making.
  • While not required, experience in technology, consulting, or finance is preferred.