Meta Data Scientist Interview guide

The Meta Data Scientist interview is demanding because the role itself sits very close to the center of how Meta operates. Data Scientists are embedded directly in product teams and work on systems that touch billions of people across Facebook, Instagram, WhatsApp, Messenger, Threads, and Reality Labs, often influencing product strategy and investment decisions that ship quickly and then keep evolving long after launch.

The compensation reflects that level of responsibility, but more importantly, the role expects you to think and operate like a product owner with a strong analytical mindset, framing problems that are not clean, choosing what to measure when there are many plausible options, and helping teams decide what to do next when the data is incomplete or uncomfortable.

Because of this, Meta ends up hiring across a spectrum of closely related roles. Data Scientists work alongside Machine Learning Engineers and Research Scientists, often on overlapping problems, but with different emphasis on modelling depth, system ownership, or research novelty, while still sharing accountability for impact at scale.

Many of the expectations described here also apply, at a higher scope and decision level, in the Meta Data Science Manager Interview, where execution is evaluated alongside influence and long-term judgment.

Well, nobody says it better than Meta themselves.

Read this guide to get oriented and understand what you’re walking into. Read the deep dives to pick up the habits, instincts, and analytical frameworks the interviews reward.

All the best!

Meta Specific Context

• Meta Newsroom
• Culture at Meta
• Interviewing at Meta: The Keys to Success

Interview Prep

• Initial Screening Deep Dive
• Product Analytics Deep Dive
• Technical Skills Round Deep Dive
• Analytical Execution Round Deep Dive
• Analytical Reasoning Round Deep Dive
• Most recently asked Meta Data Scientist interview questions with community answers (FREE)
• Meta Data Scientist Expert Mock Interviews

SQL

• PostgreSQL
• https://sqlzoo.net/
• SQL Course
• Programmer Interview SQL Practice Database
• Mode Analytics SQL Tutorials
• SQL Tutorial

Coding

• HackerRank
• CodingBat
• LeetCode

Analytical Bar

• Introducing Analytics at Meta
• How data scientists lead and drive impact at Meta
• How Meta tests products with strong network effects
• A Summary of Udacity A/B Testing Course

The conversation usually moves through your background, some projects you have recently shipped or supported (where you drove progress from a data science perspective), the kinds of product and business data you have worked with at scale, and the analytics tools you reach for by default such as SQL, Python, or experimentation platforms, all while the recruiter is assessing whether your story fits how Data Scientists operate day to day at Meta inside product analytics teams and cross functional product organizations.

From what we've seen of Prepfully candidates, clear ownership, concrete impact, and honest reflection tend to matter more than perfectly polished narratives, because this round is about deciding whether your profile demonstrates product judgment, data driven decision making, and execution credibility, and whether it is worth putting you in front of the rest of the Meta Data Scientist interview loop.

As a silver lining, yes, you did see this (you're welcome, hah!) and it helped you because now you will remember to:

• Drop one memorable detail that sticks. A product metric movement that surprised you, a tradeoff that sparked debate across product or engineering, or a decision that felt uncomfortable at the time, because recruiters look not just for titles, they look for texture, and this round is about deciding whether to advocate for you internally as a product focused data scientist.
• Show comfort with iteration. It's OK if you didn't close stuff, as long as you showed you iterated aggressively and drove learnings and progress.
• When you are talking through a project, do not stop at the first result. The interesting part is almost always what happened next. Talk about what you revisited after seeing the initial signal, what ended up getting scaled or rolled out more broadly, and what had to be corrected or walked back later. That kind of framing quietly signals that you are comfortable with experimentation, post launch analysis, and thinking about long term product impact rather than one off wins.
• It also really helps to make it obvious that you have worked with real people. Casual mentions of a product manager pushing back on scope, an engineer questioning feasibility, or leadership asking for a faster answer do a lot of work for you. They signal cross functional collaboration, stakeholder alignment, and an understanding of how product decisions get made at Meta, which is rarely in isolation.

Interview Questions

• Why do you want to join Meta?
• Why do you think you will be a good fit for the role?
• What responsibilities do you expect to have from your job at Meta?
• What makes you the best candidate for this position?

The initial screening is the first real technical round in the Meta Data Science interview process. It usually happens one to three weeks after the recruiter screen and is run by a senior Meta Data Scientist or Product Data Scientist. The goal is to see whether your analytical execution, SQL skills, and product analytics fundamentals line up with how work actually happens on Meta product teams.

The whole thing runs live in CoderPad and moves through two parts back to back.

Data manipulation is the fast reality check. You are working with real product data, writing SQL without much warm up, and explaining your thinking as you go. The bar here is clean logic, correct answers, and comfort operating under a bit of time pressure.

Product and metrics is where the conversation opens up. You are given a product situation and asked to decide which metrics matter, how to reason through tradeoffs and uncertainty, and how you would turn messy data into a decision someone would trust to act on.

Interview Questions

• Imagine you're analyzing Reels watch time. Write a SQL query to find the top 5 Reels creators (by total watch time) in the last 7 days, excluding creators who started less than 30 days ago. Also rank them within each country.
• Can you describe a particularly challenging data analysis project you worked on in the past? What were some of the key challenges you faced and how did you overcome them?
• Given a database schema and data, write a SQL query to extract specific information required to answer a given product-based question.

If the initial round still feels like “just SQL and metrics,” the Initial Screening deep dive shows where that illusion breaks, and how SQL, metrics, and live reasoning are judged in real time.

The Product Analytics deep dive elaborates part of the interview where priorities collide, metrics fail to rescue you, and you find out whether you can defend a decision when there is no clean answer left.

This round is about execution in the most literal sense. You are given a concrete product analytics problem, often tied to a metric movement or a system level issue, and asked to work through it end to end using SQL or Python while talking through your reasoning live.

The discussion usually moves from framing to analysis to interpretation. You might adjust assumptions, rewrite queries, or change direction as new constraints come up. What Meta looks for is analysis that feels steady and reusable, and execution that could hold up in a real product review without needing extra explanation.

Interview Questions

• Given a dataset on user engagement, how would you structure a solution to improve user interaction on our platform?
• Code a solution to analyze customer behavior data and identify patterns that could inform marketing strategies.
• Consider a scenario where user behavior is drastically different on weekends. How would you modify your analysis to account for this edge case?

The Technical Skills Round Deep Dive includes clear sections on what Meta expects at each step, insider context on how interviewers interpret your choices in real time, and concrete examples of what separates acceptable execution from analysis that truly earns trust inside a product review.

These 45 minutes are structured, technical, and deliberately paced, with the interviewer typically starting from a concrete feature change or experiment and asking you to work through it step by step, often beginning with how you would frame the hypothesis and define success in terms that could realistically support a launch decision.

You are expected to be precise with language and notation. When you say “lift” or “impact”, the interviewer will assume you mean something well defined. When you talk about metrics, they expect you to know whether you are discussing means, medians, distributions, or percentiles, and why that choice matters for the product being discussed. Vague answers tend to get gently but persistently narrowed until only correct reasoning remains.

Interview Questions:

• We're running an A/B test to increase Reels watch time with a new ranking algorithm. What's the null hypothesis? Alternative? Is this one-tailed or two-tailed? And how many samples do we need?
• If we lower our significance threshold from p < 0.05 to p < 0.10, what changes? Should we do it?
• Your test shows a +0.1% statistically significant lift in revenue with N=1M users. Is this launch-worthy?

Now that you have a high level overview of what the round tests for, head to The Analytical Execution deep dive. It’s a detailed, authoritative guide that tell you what it is like when you are in the room and the interviewer starts tightening definitions, pushing on confidence, and asking whether your analysis is strong enough to act on instead of just correct.

This round closely mirrors the evaluation style used in the Meta Data Science Manager Interview.

This is a 45 minute onsite round and it usually carries the highest technical bar in the Meta Data Scientist loop, not because the math is harder but because there is nowhere to hide behind it. The interviewer brings a product or system situation and talks it through with you at a high level, deliberately without datasets, dashboards, or numbers to crank through, because they are far more interested in how your reasoning holds together than in how fast you can calculate.

The conversation moves as you do. You are expected to think out loud, make your assumptions visible, and connect data signals back to real product decisions as if this were an actual review rather than a test. Partway through, constraints will show up or earlier assumptions will get nudged, sometimes gently and sometimes not, and the real tell is whether your thinking stays grounded and coherent as the problem shifts, or whether it only worked back when the setup was friendlier.

Interview Questions:

• Marketplace sellers who list items on weekends have 30% higher sales. Should we offer them a seller bonus to list on weekends
• You're testing a new creator bonus program (to increase Reels supply). How do you design the experiment to measure true incremental impact, not just intra-creator effects?
• Your test shows engagement +8% (win!), but creator satisfaction -2% (slight loss). Your PM asks: 'Should we launch?' How do you present this?

The Analytical Reasoning Round Deep Dive explains how Meta tests causal reasoning, system thinking, and comfort with ambiguity when there are no numbers to lean on and no clean answers to hide behind. If you skip this, you are relying entirely on instinct under pressure, which is not a strategy.

What matters most to Meta is how you navigated the space between incomplete information, competing incentives, and real consequences, anything beyond planning, essentially.

There is also a strong focus on how responsibility shows up in your narrative. Not in the sense of blame or credit, but in whether you speak as someone who actively shaped outcomes or as someone who observed them happen. Meta places a lot of weight on candidates who can clearly articulate what they owned, what they influenced, and what they consciously chose not to take on, because that clarity maps directly to how work gets scoped internally.

If you want to really get a handle on how this round works, it helps to go back to where the modern version of it was shaped in the first place. Amazon more or less turned behavioral interviewing into an operating system, complete with Leadership Principles and a shared grammar for telling stories, which is why so many interviewers across the industry now expect answers that sound structured, grounded, and anchored in real decisions rather than opinions.

Read our Amazon Behvaioral Interview Guide here

Meta is not looking for Amazon shaped answers, and you should not assume you can recycle stories unchanged even if you are interviewing at both companies. The values being tested are different, the tradeoffs that matter are different, and the way success is defined inside the story needs to match the place you are interviewing for.

However, here are some Meta-specific Leadership questions that DSes who recently interviewed there have reported to Prepfully:

• Tell me about a time when requirements changed mid-project. How did you adapt?
• Tell me about a time you disagreed with a teammate. How did you handle it?
• Describe a time you ensured all team members felt included in a project.

Before negotiating, check market pay for your level so you have context for base, bonus, and stock. Negotiation usually starts after the first offer, and it is fine to ask for time or come back with a counter, while recruiters may raise compensation ranges earlier, so think in ranges until you see the written offer.

When you negotiate, focus on the key words that matter: total compensation, base salary, stock equity or RSUs, signing bonus, and annual bonus. If your initial offer is below what you expected based on Levels.fyi data, you can politely explain how your skills and experience justify compensation at or above the median or higher range. Keep the conversation professional and flexible, showing that you understand the different components of an offer and that you are interested in a win-win outcome.