Meta IT Data Scientist interview questions & guide 2026

Preparing for a Meta Data Science interview requires a shift in mindset. You are not just being tested on your ability to write code; you are being evaluated on your ability to solve business problems using data.

Key Evaluation Criteria:

• Product Intuition (Product Sense) – This is often the most critical and challenging component. Interviewers assess your ability to translate vague business questions into concrete metrics. You must demonstrate how you would measure success, diagnose metric movements (e.g., "Why did Likes drop by 10%?"), and make launch decisions.
• Technical Execution (SQL & Coding) – You must demonstrate fluency in data manipulation. Meta expects you to write clean, optimized SQL (or Python/Pandas) to extract and transform data. The focus is on correctness and efficiency in solving realistic data retrieval problems.
• Analytical & Statistical Rigor – You need a strong grasp of applied statistics. This includes hypothesis testing, designing A/B experiments, understanding statistical significance, and selecting the right machine learning models for specific problems (e.g., classification vs. regression).
• Communication & Influence – You will be tested on your ability to explain complex technical concepts to non-technical stakeholders. The "Behavioral" rounds focus on how you handle conflict, drive impact, and align with Meta’s core values.

The interview process at Meta is standardized yet rigorous, designed to minimize bias and maximize signal on your core competencies. Based on recent candidate experiences, the process is generally efficient but demands significant preparation.

Typically, the process begins with a recruiter screen to assess your background and interest. This is followed by a Technical Screen (usually video), which focuses on a mix of SQL coding and basic product/analytical questions. If you pass this stage, you move to the Virtual Onsite (the "Loop").

The Onsite usually consists of 4–5 separate interviews. For the standard Data Science track, expect a heavy emphasis on Product Analytics (Case Studies) and SQL. However, depending on the specific team (e.g., Core ML, Ads, or XR Insight), the process may skew heavily toward Machine Learning design and algorithmic coding. You should clarify with your recruiter whether your loop is "Product-focused" or "Systems/ML-focused," as recent reports indicate variations where candidates faced deep coding challenges and ML system design questions.

The timeline above illustrates the typical progression from initial contact to the final decision. Use this to pace your study schedule: focus on SQL and basic stats for the screen, then broaden your preparation to include deep product cases and behavioral stories for the onsite phase. Note that the gap between the screen and the onsite is the best time to practice "mock" interviews for the Product Sense round.

Meta’s interview loops are structured to test specific competencies in isolation. You should prepare for distinct "modules" of questioning.

Product Interpretation & Analytics (The "Case" Study)

This is the defining interview for Meta Data Scientists. You will be given an open-ended scenario regarding a Meta product.

• Why it matters: It simulates your daily work—taking an ambiguous problem and structuring a data-driven solution.
• Strong performance looks like: A structured approach (Frameworks like CIRCLES are helpful). You should clarify the goal, define the user, propose specific metrics (North Star vs. Counter-metrics), and design an experiment.

Be ready to go over:

• Metric Definition – How to measure "engagement," "success," or "quality."
• Debugging Metrics – Investigating sudden changes in data (e.g., "Time spent on Instagram Reels dropped 5% yesterday. How do you investigate?").
• Experimentation – Designing A/B tests, choosing randomization units, and determining sample sizes.

Technical Skills (SQL & Algorithms)

You will have a dedicated coding round. For general DS roles, this is primarily SQL. For ML-focused roles, this includes Python/Algorithms.

• Why it matters: You must be able to pull your own data.
• Strong performance looks like: Writing syntax-perfect SQL on a whiteboard or shared doc without an IDE. Handling edge cases (NULLs, duplicates) and using advanced joins/window functions.

Be ready to go over:

• SQL Joins & Aggregations – Self-joins, LEFT JOIN vs INNER JOIN, GROUP BY.
• Window Functions – RANK(), LEAD(), LAG(), and running totals.
• Algorithmic Coding (ML Track) – LeetCode Easy/Medium questions involving arrays, strings, or hashmaps.

Applied Statistics & Machine Learning

Depending on the role, this can range from basic probability to full ML system design.

• Why it matters: You need to validate your insights and build predictive models.
• Strong performance looks like: clearly explaining why you chose a specific model or test, not just the math behind it.

Be ready to go over:

• Probability – Bayes' Theorem, expected value.
• Statistical Inference – P-values, confidence intervals, bias vs. variance.
• Machine Learning – Logistic regression, Random Forests, overfitting/underfitting, and Recommendation Systems (specifically for News Feed/Ads roles).

As a Data Scientist at Meta, your day-to-day work is a mix of deep technical analysis and high-level strategy. You are responsible for the entire data lifecycle: from instrumentation (logging) to analysis and final recommendation.

You will spend a significant portion of your time defining and monitoring metrics. You will collaborate with engineering to ensure the right data is being captured. Once data is available, you will build pipelines (using SQL/Presto/Spark) to aggregate this data into dashboards that track product health.

Beyond reporting, you are the voice of data in product reviews. You will design and analyze A/B tests to validate new features. If a test result is ambiguous (e.g., clicks went up, but sessions went down), it is your job to dig into the data, segment by user type, and provide a launch/no-launch recommendation.

For roles in specialized teams like XR Insight or Ads, you may focus more on building and tuning machine learning models to personalize content or improve ad targeting efficiency. This involves feature engineering, model training, and offline/online evaluation.

Candidates who succeed at Meta typically possess a strong quantitative background combined with a "builder" mindset.

• Technical Skills (Must-Have):

  • SQL: Expert level. You must be able to write complex queries from scratch.
  • Python or R: Proficiency for data manipulation (Pandas) and statistical analysis.
  • Statistics: Solid understanding of hypothesis testing and experimentation.

• Technical Skills (Nice-to-Have / Role Specific):

  • Machine Learning: Experience with scikit-learn, TensorFlow, or PyTorch (essential for ML tracks).
  • Big Data Tools: Experience with Hive, Spark, or Presto.

• Soft Skills:

  • Communication: Ability to synthesize complex data into a clear narrative for leadership.
  • Product Sense: An innate understanding of what makes a product successful and how users interact with social platforms.

• Experience Level:

  • Meta hires across all levels (University Grad to Director). However, for standard DS roles, 2+ years of analytics experience in a consumer technology context is often preferred.

Q: Is the coding round strictly SQL or can I use Python? For the general Analytics track, SQL is the primary language for data retrieval questions. However, for data manipulation or algorithmic questions, you can typically use Python or R. Always clarify with your recruiter which track (Analytics vs. ML) you are interviewing for, as the ML track will require Python.

Q: How difficult is the interview process? Candidates rate the difficulty as Medium to Hard. While the questions are not "trick" questions, the bar for communication and structure is very high. You are expected to drive the conversation. The "Product Sense" round is often cited as the most difficult because it is ambiguous and open-ended.

Q: What is the difference between a Data Scientist and a Research Scientist at Meta? A Data Scientist generally focuses on product analytics, metrics, and A/B testing to drive product decisions. A Research Scientist (or Core ML DS) often focuses more on developing novel machine learning algorithms, deep learning, and long-term research initiatives. The interview for Research Scientist is significantly more technical/algorithmic.

Q: Does Meta offer remote work for Data Scientists? Meta has embraced a distributed working model, but this varies by team and location. Many roles are hybrid, requiring some days in the office, while others may be fully remote depending on the specific organization's policy.

• Clarify Before You Solve: In the coding and SQL rounds, never start writing code immediately. Ask questions to clarify the schema, edge cases, and expected output format. This shows maturity and prevents you from solving the wrong problem.
• Think Out Loud: Silence is a red flag. Whether you are debugging a metric or writing a join, explain your thought process. Interviewers will often give you hints if they understand your logic, as noted in recent candidate experiences.
• Know the Product: You will likely be asked about Facebook, Instagram, or WhatsApp. Spend time using these apps critically before the interview. Ask yourself: "Why is this button here?" "What metric are they trying to optimize?"
• Structure Your Case Answers: For product questions, use a framework. Start with the Goal, then User Segments, then Actions, then Metrics, and finally Trade-offs. Wandering answers without structure are heavily penalized.
• Prepare for "The Gap": Be ready to explain the gap between a model's theoretical performance and its business impact. Meta cares about applied data science.

The Data Scientist role at Meta is a premier opportunity to work on products that define the social internet. It is a role that demands a rare combination of technical excellence, statistical literacy, and product vision. The interview process is designed to find candidates who can not only pull data but also tell a compelling story with it to drive business strategy.

To succeed, focus your preparation on SQL fluency (speed and accuracy) and Product Sense frameworks. Do not neglect the behavioral aspect; Meta places high value on candidates who are collaborative and can navigate ambiguity. If you are interviewing for a specialized team, ensure your ML fundamentals are sharp.

The salary data above reflects the high demand for this skillset. Compensation at Meta is typically composed of a strong base salary, a performance bonus, and a significant equity component (RSUs). For Data Scientists, the equity portion can be substantial, rewarding long-term impact and retention.

With structured preparation, you can navigate this process successfully. Approach the onsite as a series of collaborative discussions rather than interrogations. Good luck!