Meta ML Research
Interview Guide

📊 Quick Stats

Timeline: 6-8 weeks | Difficulty: Hard | Total Comp (IC4): $185-250K | Reapply: 12 months

What makes it unique: Committee-based hiring • Team matching after approval • Heavy culture emphasis

The Gist

Meta's analytics interview process stands apart from other tech giants in several key ways. Unlike traditional hiring where your interviewers make the final decision, Meta uses a committee-based system where an independent group reviews your performance. This reduces individual bias but also means you won't get direct feedback from the people who interviewed you.

Another unique aspect is the team matching phase that happens after you're approved. You don't interview for a specific role like "Instagram Analytics" or "WhatsApp Metrics"—instead, you interview for "Meta Analyst" broadly, then get matched with teams that fit your interests and skills. This gives you leverage to find the right cultural and technical fit.

The behavioral "Jedi" round carries significant weight at Meta—more than at most companies. Strong technical skills won't save you if you don't align with Meta's cultural values like "Move Fast" and "Focus on Impact." The company genuinely prioritizes speed over perfection, and interviewers are trained to spot candidates who might slow teams down with overthinking or perfectionism.

Expect the entire process to take 6-8 weeks, which is faster than many peers. Meta values decisive action, and this extends to their hiring timeline. Strong SQL skills, sharp product sense, and authentic alignment with their values are your tickets through the door.

What Does a Meta Data Analyst Do?

As a data analyst at Meta, you'll transform billions of data points into insights that shape products used by over 4 billion people globally. This isn't about creating weekly reports for executives—it's about being embedded with product teams, where your analysis directly influences what billions of users see and experience on Instagram, WhatsApp, Facebook, and Reality Labs.

Your day-to-day work involves analyzing user behavior at massive scale, designing and evaluating A/B experiments, building metrics frameworks that teams rely on, and investigating why metrics move up or down. You'll partner closely with product managers and engineers, often sitting in the same room (or Zoom) as they make product decisions. When Instagram wants to roll out a new feature, you're the one determining whether it's actually working.

The technology stack centers around Presto (Meta's distributed SQL engine), Python for data manipulation, and various internal dashboarding and experiment platforms. You'll learn these proprietary tools on the job—what matters in the interview is demonstrating strong SQL fundamentals and analytical thinking.

Career levels follow Meta's IC (Individual Contributor) track: IC3 for early-career analysts (0-2 years, $150-180K total comp), IC4 for mid-level (2-5 years, $185-250K), and IC5+ for senior roles (5+ years, $240K+). Each level bump represents not just more experience, but increasing autonomy, scope of impact, and complexity of problems you can tackle independently.

Before You Apply

What Meta Looks For

Meta evaluates candidates on both technical capabilities and cultural fit, with neither being sufficient on its own. On the technical side, they expect advanced SQL proficiency—not just writing queries, but optimizing them, understanding when to use window functions versus subqueries, and reasoning through complex data transformations. You'll need solid product intuition to define meaningful metrics and statistical fundamentals for designing proper A/B tests. Python skills (pandas, numpy) are valuable but SQL is the true gatekeeper.

Behaviorally, Meta seeks people with a bias for action who thrive in ambiguous situations rather than freezing up when the path isn't crystal clear. They want to see an impact-driven mindset where you naturally quantify your results ("increased engagement by 15%" not just "improved engagement"). Strong communication skills matter enormously—you'll need to explain technical concepts to product managers and business stakeholders who don't write SQL.

Red flags that will sink your candidacy: Perfectionism that slows down teams, working in silos instead of collaborating, missing the business context behind technical work, and being defensive when receiving feedback. Meta interviewers are trained to probe for these patterns, especially in the behavioral "Jedi" round.

Prep Timeline

💡 Key Takeaway: Start SQL practice 3+ months early. Meta's SQL questions test real-world scenarios, not trivia—you need time to build pattern recognition.

3+ months out:

• Grind SQL on LeetCode, HackerRank, or Skillvee
• Focus on: window functions, cohort analysis, funnel queries, retention calculations
• Study Meta's products deeply (use Instagram, Facebook, WhatsApp daily)

1-2 months out:

• Practice product sense questions (Exponent, IGotAnOffer)
• Prepare STAR stories for behavioral questions aligned with Meta's values
• Mock interviews with peers or coaches

1-2 weeks out:

• Review your stories and ensure quantified impact
• Practice thinking out loud for SQL problems
• Research the teams you'd be interested in

Interview Process

⏱️ Timeline Overview: 6-8 weeks total (can extend to 12+ if team matching takes time)

Format: 1 recruiter call → 1 technical screen → 4-5 hour onsite → committee review → team matching

Meta's analytics interview has 4-5 stages:

1. Recruiter Screen (30-45 min)

Quick phone call to assess basic fit, discuss your background, and gauge interest.

Questions:

• "Why Meta?"
• "Walk me through your resume"
• "What's your timeline?"

Pass criteria: Clear communication, relevant experience, genuine enthusiasm.

2. Technical Phone Screen (45-60 min)

This live coding session is where many candidates stumble—not because the SQL is impossibly hard, but because they freeze up or work in silence. You'll typically face 2-3 SQL problems that ramp up in difficulty, plus one product or metrics question to test your business judgment.

The SQL questions aren't designed to trick you with obscure syntax. Instead, they test whether you can translate a business question into clean, logical code. You might be asked to calculate 7-day retention by signup cohort, find the top 3 products by revenue in each category, or identify users who made purchases in 3 consecutive months. These are real problems Meta analysts solve daily.

The product question assesses whether you think like an analyst or just a query writer. Questions like "How would you measure success for Instagram Reels?" or "DAU dropped 5% yesterday—how do you investigate?" reveal whether you understand the why behind the what.

🎯 Success Checklist:

• ✓ Think out loud—silence = red flag
• ✓ Ask clarifying questions before coding
• ✓ Write clean, commented SQL
• ✓ Test your logic verbally with sample data
• ✓ Explain your thought process throughout

3. Virtual Onsite (4-5 hours)

📋 What to Expect: 4-5 back-to-back 45-minute interviews

Breaks: Usually 5-10 min between rounds

Format: Video call with shared coding environment

The onsite covers both technical depth and cultural fit across multiple rounds:

Round 1: SQL Deep Dive (45 min)

Focus: Technical coding ability

• 2-3 progressively harder SQL problems
• Expect: window functions, self-joins, recursive CTEs, optimization discussions
• Example: "Build a funnel showing drop-off rates at each stage"

Round 2: Product Sense / Metrics (45 min)

Focus: Business judgment and analytical thinking

• Define metrics, design A/B tests, recommend actions
• Example: "Instagram wants to increase Stories engagement—how do you measure success and design an experiment?"

Round 3: Behavioral - "Jedi" Round (45 min)

Focus: Cultural fit and Meta values alignment

• Deep dive into past experiences using Meta's values
• Sample questions:
  • "Tell me about a time you moved fast with incomplete information"
  • "Describe a situation where you challenged a decision with data"
  • "Give an example of work that created impact beyond your team"

💡 Pro Tip: Prepare 5-7 STAR stories covering each of Meta's core values. This round can make or break your candidacy regardless of technical performance.

Round 4: Analytics Case Study (45 min)

Focus: End-to-end problem-solving

• Business problem requiring structured thinking
• Example: "Should WhatsApp build a new feature? Walk through your analysis approach."

Round 5 (Optional): Leadership/Technical Depth (45 min)

Focus: Senior-level evaluation

• For IC4+ roles
• Deep dive into projects or leadership scenarios

4. Hiring Committee Review (5-7 days)

Your interview feedback goes to an independent committee who decides:

• ✅ Hire (move to team matching)
• ❌ No hire (12-month cooldown)
• 🔄 Additional interview needed (rare)

The committee also determines your level (IC3, IC4, IC5) based on performance.

5. Team Matching (1-4 weeks)

Once approved, you'll have calls with 1-3 potential teams/managers. This is mutual selection—you choose your team.

Questions to ask:

• "What are the team's priorities in the next 6 months?"
• "What does success look like in the first 90 days?"
• "How does analytics influence product decisions here?"
• "What's the team culture like?"

Note: Approval expires after ~6 months if you don't match.

Key Topics to Study

SQL (Critical)

⚠️ Most Important: Window functions appear in nearly every Meta SQL interview. Master ROW_NUMBER, RANK, LAG/LEAD, and rolling aggregates.

Must-know concepts:

• JOINs (inner, left, self-joins)
• Window functions (ROW_NUMBER, RANK, LAG/LEAD, rolling aggregates)
• CTEs and subqueries
• Aggregations with GROUP BY, HAVING
• CASE statements and conditional logic
• Date/time manipulation
• Query optimization basics

Practice platforms: LeetCode SQL, HackerRank, Skillvee, DataLemur

Product & Metrics (Critical)

Frameworks:

• Metric definition (precision, consistency, actionability)
• A/B test design (hypothesis, sample size, significance)
• Root cause analysis (data quality → segmentation → external factors)
• Dashboard design (audience-first, minimize cognitive load)

Common Meta metrics:

• DAU/MAU, engagement rate, retention cohorts
• Content creation rate, distribution metrics
• Monetization (ads, revenue per user)

Statistics & A/B Testing (Important)

Core concepts:

• Hypothesis testing, p-values, confidence intervals
• Type I/II errors, statistical power
• Sample size calculation
• Multiple testing corrections

Red flags to avoid:

• Peeking at results early
• Ignoring seasonality
• Confusing statistical vs practical significance

Behavioral Questions (Critical)

Prepare 5-7 STAR stories covering Meta's values:

Move Fast:

• "Tell me about a time you had to decide quickly with incomplete data"

Focus on Impact:

• "What's the most impactful project you've worked on?"

Be Bold:

• "Describe a time you challenged a product decision with data"

Build Social Value:

• "Tell me about work that had impact beyond your immediate team"

Be Open:

• "Tell me about difficult feedback you received and how you responded"

Structure: Situation → Task → Action → Result (with quantified impact)

Compensation (2025)

💰 Total Compensation Breakdown

All figures below represent total annual compensation (base salary + stock + bonus)

Location Adjustments:

• 🌉 Bay Area: 1.00x (baseline)
• 🌲 Seattle: 0.95x
• 🗽 NYC: 0.98x
• 🤠 Austin: 0.85x
• 🏠 Remote: 0.75-0.90x

🎯 Negotiation Strategy:

• Stock and sign-on bonuses are most flexible (base is relatively fixed)
• Competing FAANG offers provide strongest leverage
• Focus negotiations on total comp, not just base salary
• Realistic increase with strong negotiation: $20-50K

Benefits Package:

• Unlimited PTO (typical usage: 15-20 days/year)
• Free food (breakfast, lunch, dinner on campus)
• $7-10K annual education/learning budget
• 4 months paid parental leave
• 401(k) match + ESPP (Employee Stock Purchase Plan)

Your Action Plan

Ready to start preparing? Here's your roadmap:

📚 Today:

1. Assess your current SQL level with a few practice problems
2. Review Meta's products (Instagram, WhatsApp, Facebook) - think about metrics
3. Start gathering your past experiences for STAR stories

📅 This Week:

1. Set up a study schedule (3-6 months depending on your timeline)
2. Create a practice plan for SQL fundamentals
3. Draft 5-7 STAR stories aligned with Meta's values

🎯 This Month:

1. Complete 20-30 SQL problems (focus on window functions)
2. Practice 5-10 product sense questions out loud
3. Schedule mock interviews with peers or a coach

🚀 Ready to Practice?

Browse Meta-specific interview questions and take practice interviews to build confidence and get real-time feedback.