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Meta Research Scientist Interview Guide

Meta Platforms, Inc. (Meta), formerly known as Facebook Inc., is a global leader in social technology. Since its inception in 2004, Meta has continuously revolutionized how people connect and engage with the world through platforms like Messenger, Instagram, and WhatsApp. Currently, Meta is pioneering the next evolution in social technology through immersive augmented and virtual reality experiences.

The Research Scientist role at Meta offers a unique opportunity to be at the forefront of innovation in fields like computer vision, machine learning, and AI. As a Research Scientist, you will collaborate with world-class teams to solve complex problems, develop cutting-edge algorithms, and transform Meta’s products and services to better serve billions of users worldwide.

This guide will walk you through the interview process for the Research Scientist position at Meta, including what to expect, common questions, and valuable preparation tips. Let’s get started on your journey to joining one of the most dynamic and impactful tech companies in the world!

Meta Research Scientist Interview Process

Submitting your application.

The first step is to submit a compelling application that reflects your technical skills and interest in joining Meta. Whether you were contacted by a Meta recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Meta Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Meta hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call usually spans about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Meta’s technical screenings are typically conducted virtually, involving video conference and screen sharing. Questions in this interview stage might revolve around machine learning, statistics, programming, and Meta’s specific systems.

Depending on the role, you might encounter coding exercises, algorithmic problem-solving, and data structure questions. Reviewing data structures, algorithms, and key concepts in your area of expertise is highly recommended.

Presentation Round

Upon passing the technical screening, you may be asked to deliver a presentation. This presentation aims to assess your ability to communicate complex ideas effectively and demonstrate your expertise. Typical topics for the presentation might include past projects, a specific problem area, or hypothetical scenarios related to Meta’s domain.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop, which might now be virtual depending on Covid-19 restrictions. Multiple interview rounds, varying by role, will be conducted during your day at the Meta office. These interviews will evaluate your technical prowess, including programming, machine learning capabilities, and problem-solving skills.

If you were assigned take-home exercises, a presentation round may also be included during the onsite interview.

Quick Tips For Meta Interview Success

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Meta interview include:

• Be Prepared for a Technical Deep Dive: Meta interviews are often deep dives into your technical skillset. Brush up on algorithms, data structures, and system design. Use platforms like LeetCode to practice coding challenges.
• Demonstrate Problem-Solving Skills: Meta values your problem-solving abilities. Be ready to tackle complex problems and explain your thought process clearly. Highlight how you approach problem-solving in your past experiences.
• Communicate Effectively: Your ability to communicate complex ideas succinctly and clearly is crucial. Practice delivering concise and impactful presentations, and be ready to discuss your projects and ideas thoroughly.

Start preparing and learn about how to prepare for your interview with Meta’s interview guide, tips, and interactive experiences available on their website. Visit Meta interview prep for more details.

Meta Research Scientist Interview Questions

Typically, interviews at Facebook vary by role and team, but commonly Research Scientist interviews follow a fairly standardized process across these question topics.

1 - Would you think there was anything fishy about the results of an A/B test with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect any issues with these results?

2 - What would you do if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. How would you address this issue?

3 - What metrics would you use to determine the value of each marketing channel? Given all the different marketing channels and their respective costs at a company selling B2B analytics dashboards, what metrics would you use to evaluate each channel’s value?

4 - How would you test if changing Facebook’s composer feature to a “+” button is a good idea? Facebook wants to change the user interface of the composer feature to a “+” button at the bottom of the page. How would you test if this change is beneficial?

5 - What are the Z and t-tests, and when should you use each? Explain what Z and t-tests are, their uses, the differences between them, and when to use one over the other.

1 - Write a function find_bigrams to return a list of all bigrams in a sentence. Write a function called find_bigrams that takes a sentence or paragraph of strings and returns a list of all its bigrams in order. A bigram is a pair of consecutive words.

2 - Write a query to find out how many users have opened an email. Given a table called events that keeps track of every user’s actions, write a query to find out how many users have opened an email.

3 - Write a query to select the top five most expensive projects by budget to employee count ratio. Given two tables, projects and employee_projects , write a query to select the five most expensive projects by budget to employee count ratio, accounting for duplicate rows in the employee_projects table.

4 - Write a query to get the last transaction for each day from a table of bank transactions. Given a table of bank transactions with columns id , transaction_value , and created_at , write a query to get the last transaction for each day. The output should include the id, datetime, and transaction amount, ordered by datetime.

5 - Write a query to get the average order value by gender. Given three tables representing customer transactions and customer attributes, write a query to get the average order value by gender. Round the answer to two decimal places.

1 - What is a confidence interval for a statistic and why is it useful? A confidence interval provides a range of values within which a population parameter is expected to lie, with a certain level of confidence. Explain its usefulness and how to calculate it.

2 - What are Z and t-tests, and when should you use each? Describe the Z and t-tests, their purposes, differences, and scenarios for appropriate use.

3 - Is it worth playing a game where you win $21 if the sum of two dice equals seven, but pay $10 per roll? Analyze the expected value of the game to determine if it is worth playing.

4 - How would you explain a p-value to a non-technical person? Provide a simple and clear explanation of a p-value for someone without a technical background.

5 - What is the expected number of good ads rated by different types of raters? 1. Calculate the expected number of good ads if 100 raters each rate one ad independently. 2. Calculate the expected number of good ads if one rater rates 100 ads. 3. Determine the probability that a rater was lazy if an ad is rated as bad.

1 - What metrics would you use to track accuracy and validity of a spam classifier model? Assume you have built a V1 of a spam classifier for emails. What metrics would you use to evaluate the model’s accuracy and validity?

2 - How would you evaluate the success of advertising for an event with a 10% weekly increase in search clicks? You are tracking the success of advertising for an event, and there has been a 10% weekly increase in search clicks. Is this good or bad? How would you determine if the advertising needs improvement?

3 - How does random forest generate the forest and why use it over logistic regression? Explain how a random forest algorithm generates its forest. Additionally, discuss why you might choose random forest over other algorithms like logistic regression.

4 - How would you build a restaurant recommender on Facebook and what are potential concerns? Describe how you would gather data and build a restaurant recommender system on Facebook. What are some potential downfalls or concerns with adding this feature?

5 - How would you test if having more friends increases the probability of being an active Facebook user after 6 months? Design a test to determine whether having more friends now increases the likelihood that a Facebook member remains active after 6 months.

Average Base Salary

Average Total Compensation

View the full Research Scientist at Meta salary guide

FAQ for Meta’s Research Scientist Position

Q: what is the interview process like at meta for a research scientist position.

A: The interview process at Meta involves an initial HR phone screening, followed by technical interviews comprising machine learning, statistics, and behavioral questions. A presentation may also be required, and the process typically includes multiple rounds—all designed to evaluate your skills and cultural fit.

Q: What types of questions should I expect during the technical interview?

A: Be prepared for a mix of machine learning, statistics, and behavioral questions. Your problem-solving abilities will be tested with coding challenges, often sourced from platforms like LeetCode. Dive deep into your past projects and experiences—this is your time to shine!

Q: What qualifications are Meta looking for in a Research Scientist?

A: Meta values strong academic backgrounds in Computer Science, Computer Vision, or related fields, often requiring a Ph.D. Additionally, your experience with machine learning frameworks like TensorFlow or PyTorch, and a proven track record of research via publications or patents, are essential.

Q: How is the company culture at Meta?

A: Meta champions a culture of innovation, diversity, and learning. You will be encouraged to take risks, think creatively, and collaborate with a global team. It’s a fast-paced environment where your contributions can have a significant impact on the future of technology.

Q: How should I prepare for my interview at Meta?

A: Preparation is key! Focus on mastering your technical skills and practicing coding problems. Familiarize yourself with Meta’s products and services. Brush up on your knowledge of machine learning and statistics, and be ready to articulate your experiences and research effectively. Confidence is crucial, so practice thoroughly and stay positive!

As the landscape of social technology continues to evolve, Meta remains at the forefront, seeking innovative and visionary research scientists to propel the next generation of immersive experiences. The path to joining Meta is methodical yet thrilling, involving stages like initial screenings, technical interviews covering machine learning and statistics, and comprehensive presentations.

Meta’s interviewers are notable for their professionalism and friendly demeanor, making a potentially stressful process more approachable. Despite occasional setbacks due to external factors like hiring freezes, candidates are encouraged to persist and prepare rigorously using resources like LeetCode.

By showcasing your proficiency in computer vision, machine learning, and data science, and honing your problem-solving skills, you’ll stand out in this exciting field. Embrace the journey, tap into Meta’s extensive interview preparation resources, and transform your aspirations into reality.

Good luck with your interview! Explore further by visiting Meta’s interview prep guides for more insights and tips. Dive into the world of Meta, where your research could define the future of communication and connectivity. ????

Practice Interview Questions

Meta Data Science Interview Guide [30 LEAKED Questions from 2024]

By Nick Singh

(Ex-Facebook & Best-Selling Data Science Author)

Currently, he’s the best-selling author of Ace the Data Science Interview, and Founder & CEO of DataLemur.

April 16, 2024

Meta is the best place in the world to be a Product Data Scientist – but, I’m biased! I worked on Facebook’s Growth Team and wrote a best-selling book with my bestie whose an Ex-Facebook Data Scientist. We've got a lot of love for Meta, and want to see you get hired there too!

In this article, we'll share insider tips into the Meta Product Analytics Data Science interview process, and leak share 30+ recently asked Meta Data Science interview questions. After you read this 6,000 word guide, you’ll be ready to Ace the Meta interview, just like we did back in the day:

In this Meta Data Science Interview guide, we’ll cover:

• Stages of the Interview Process
• 6 Analytical Execution Questions
• Preparing for the Analytical Reasoning (Product-Sense) Round
• 12 Analytical Reasoning Questions
• A/B Testing Questions
• Recently Asked Meta SQL Interview Questions
• Product Case Study Questions
• Meta Behavioral Interview Questions
• 7 Resources to Prepare for the Meta Data Science Interview

The Meta Data Scientist (Product Analytics) Interview Process

The interview process for Meta usually takes about 4-6 weeks. During that time you will have multiple SQL, product-sense, and analytical case study rounds. Let's dive into each part of the interview loop:

Round 1: Recruiter Screening

The 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, Understanding your Experience, Logistics

Insider Tip: Have a convincing answer ready to go for the inevitable question " Why do you want to be a Product Data Scientist at Meta? ". This might seem like an obvious and easy question, but SO MANY folks we talk to fail this because they yap on-and-on about fine-tuning LLMs and deep learning and Computer Vision. But this answer sucks because Product Analytics Data Scientists at Meta DO NOT build Machine Learning models.

A good answer to "Why Product DS @ Meta?" would incorporate:

• your passion for working cross-functionally with PMs and business stakeholders
• a story about how you defined key product metrics to better understand and track the performance of a product or business line
• a time you ran an A/B test, and the impact that experiment had on the future product roadmap
• why you want to keep doing this past product data science work, but now on a product that serves 2-billion people!

Round 2: Technical Screening

The next step after the phone screen is a virtual technical screen:

• 💼 Format: Virtual video call
• ⏰ Duration: 45 - 60 minutes
• 👤 Interviewer: Hiring Manager/Senior Data Scientist
• ❓ Questions: Technical Skills (SQL), Product case

The Meta SQL test is typically conducted on Coderpad, where the interviewer can watch you code live, similar to the DataLemur interface . The dialect of SQL you use doesn't matter much, so if you use a mainstream one like MySQL or PostgreSQL or SQL Server you should be good to go! Here's what the

Insider Tip: Meta needs you to be very fast & accurate with writing SQL. Being rusty with SQL because you use R or Python day-to-day is NOT a valid excuse at Meta. They have thousands of people who apply for this role, and the SQL screen is an easy black-and-white filter to remove candidates, so you should aim for flawless execution.

The best way to practice for the SQL technical screen is to solve real SQL interview questions asked by Meta. We covered these in our article 9 Meta/Facebook SQL Interview Questions and built an interactive coding-pad on DataLemur to help you practice:

Intro Product Sense Question As Part of Phone Screen

You'll also be asked a light "Product Sense" question as part of your technical phone screen. This question is usually related to the SQL coding question. For example, if your SQL coding question is about analyzing churn of Facebook Marketplace users, you might first be asked an open-ended metrics question like "What are some metrics you'd track to measure the health of Facebook Marketplace?"

Final Round: 4-5 Interviews On-Site

Anywhere from 1 to 3 weeks following the Technical Screen, you'll hear if you’ve moved to the next round. The Meta Virtual On-Site Data Science Interview is split into 4 interviews, each 45 minutes long focusing on a different topic:

• ⏰ Duration: 45 minutes each
• ❓ Topics: Analytical Execution, Analytical Reasoning, DS Technical Skills, & Behavioral Questions

In case you have no idea what an "analytical execution" or "analytical reasoning" round entails – you're not alone – we think Meta's terminology is weird too. Let's dive into each interview round, and some leaked questions, in the next section.

30 Meta Data Science Interview Questions

The Meta Data Science onsite interview covers:

• Analytical Execution: probability, statistics, hypothesis testing
• Analytical Reasoning: product metrics definition, evaluating tradeoffs, A/B testing
• DS Technical Skills: SQL
• Behavioral Interview Questions

Let's examine each round in more detail, and cover 30+ leaked Meta DS interview questions.

Meta Analytical Execution Round

The Analytical Execution round tests your probability skills, statistical foundations, and raw math brainpower (which Meta calls mental agility). Specific topics include:

• Elements of descriptive statistics (mean, median, mode, percentiles)
• Common probability distributions (binomial, normal, poisson)
• Combinations, Permutations, Conditional Probability, and Bayes' Theorem
• Issues analyzing real-world data (outliers, missing values, etc.)
• Key statistics concepts (Law of Large Numbers, Central Limit Theorem, etc.)
• Conditional probabilities, including

This round is quite tricky, because most Data Scientists day-to-day don't use Bayes' Theorem or do calculations involving binomial random variables. It can be exceptionally difficult for seasoned Data Scientists, who might have last touched these concepts in an undergrad stats class, 10+ years ago.

Meta doesn't give AF.

The only way to crush this round is to review your prob + stats foundations, and of course practice (starting with these next 6 questions).

6 Meta Analytical Execution Interview Questions

• On Instagram, the probability of a user watching a story to completion is 0.8. If a user posts a sequence of 4 stories, what is the probability that a viewer will watch all 4 stories? What about at least 2 stories?
• What is the difference between Type I and Type II errors in hypothesis testing?
• Say you roll a die three times. What is the probability of getting two sixes in a row?
• Can you explain what a p-value and confidence interval are, but in layman's terms?
• Explain the concept of covariance and correlation. How are they different, and what do they measure?
• A Facebook Ads analyst is investigating the effectiveness of a new ad targeting algorithm. As a general baseline, they know that 1% of all users who see an ad convert (make a purchase). The new algorithm correctly identifies 80% of users who will convert for an ad. The algorithm also incorrectly flags 10% of non-converting users as likely to convert. Given that the algorithm has flagged a user as likely to convert, what is the probability that this user will actually convert?

To practice more of these analytical execution interview questions, read Chapters 5 and 6 in Ace the Data Science Interview:

Analytical Reasoning / Product-Sense Round

The Analytical Reasoning round tests your general product-sense/business-sense. Unlike the execution round, there's usually no math – and no one right answer. Instead, Analytical Reasoning rounds usually have a long back-and-forth discussion around some specific new product or feature.

Meta might ask you:

• what data would you analyze to see if building this new product/feature is worth it?
• how would you design an A/B test for the new feature?
• what A/B testing pitfalls might you encounter?
• what success metrics would you track, to see if this new feature is good ?
• what guardrail or counter-metrics would you track?
• if some key metric went up, but a different metric got worse, how would you determine whether to ship the feature?
• if there suddenly was a drop in some key metric, how would you troubleshoot the root-cause of the metric change?

To prepare for Meta's analytical reasoning round, read our in-depth Product-Sense Interview Guide to get tips on:

• Defining a Product Metric
• Diagnosing a Metric Change
• Brainstorming Product Features
• Designing A/B Tests

You'll also want to read the Product-Sense chapter of Ace the Data Science Interview , which has 30 real product-sense questions with solutions, along with proven frameworks to tackle these open-ended product metrics & A/B testing questions.

Once you read the above resources, you're ready to tackle 12 real Meta analytical reasoning interview questions.

12 Analytical Reasoning Interview Questions

• Meta's mobile app is suddenly experiencing high bounce rates and low session durations. How would you troubleshoot this issue?
• A user advocacy group raises concerns about the accessibility of Meta's platform for individuals with hearing disabilities. What are some product improvements that could be made with Facebook Live and Facebook videos? What metrics would you define, to see if your features had a positive impact?
• Imagine you launched a feature to grow engagement of Facebook Groups. The Daily-Active-Users of groups goes up by 2%, but the average time-spent on Facebook Groups goes down by 3%. How would you determine if you should ship this feature?
• Meta is trying to launch social shopping, similar to TikTok Shop. Without building a beta-test of the feature, how would you opportunity size the revenue impact from the feature?
• Imagine Meta is planning to launch a new video feature aimed at young adults. How would you assess the product-market fit and define success metrics to ensure resonance with the target demographic?
• Meta's data science team is analyzing user engagement metrics for a new close-friends Reels tab. However, the data shows a significant drop in engagement rates shortly after the feature launch. How would you investigate the cause of the drop in user engagement, prioritize potential factors contributing to the decline, and propose data-driven strategies to address the issue?
• Meta's advertising team is exploring ways to optimize ad targeting to increase revenue and improve ad relevance for users. However, ad click-through rates are lower than expected, indicating potential issues with targeting accuracy. How would you analyze user demographic and behavioral data to assess the effectiveness of ad targeting algorithms, and what strategies would you propose to improve targeting accuracy and ad performance?
• Meta's product team is considering introducing a new feature that allows users to customize their profile settings. However, there are concerns about potential privacy implications and data security risks associated with the feature. How would you conduct a privacy impact assessment to evaluate the potential risks and benefits of implementing the new feature, and what analytical methods would you use to assess user privacy preferences and mitigate privacy concerns?
• Meta's data science team is exploring ways to improve search relevance for users navigating its marketplace platform. However, search queries are returning irrelevant or inaccurate results, leading to frustration among users. How would you analyze user search queries and click-through behavior to identify issues with search relevance?
• Meta's data science team is investigating the impact of algorithmic bias on content recommendations in its news feed. Users have reported instances of bias in recommended content, leading to concerns about fairness and diversity. How would you quantify and measure algorithmic bias in content recommendations, and what analytical techniques would you use to identify biased patterns and mitigate the impact of bias on user experience and content diversity?
• The PM responsible for Facebook events has a new idea to drive engagement – when your friends mark that they'll attend an event, you will get a notification. How would you measure the success of this notification? What counter-metrics would you look at?
• The Instagram Monetization team would love to double the amount of ads shown on Instagram – it's the quickest way to nearly double revenue over-night. What do you think about this idea? How would you determine the optimal ad-load for Instagram?

6 Meta A/B Testing & Interview Questions

Meta often goes deeper into A/B testing and Research Design questions. They might hammer the maths/stats of A/B testing in the Analytic Execution round, and cover higher-level product experimentation questions in the Analytical Reasoning rounds.

To prepare, here's 6 real A/B testing questions asked by Meta:

• Explain how you would set up a randomized controlled trial (RCT) to evaluate the effectiveness of a new privacy feature on Meta's messaging platform.
• Describe a methodological approach you would use to assess the usability of a new user interface design for Meta's virtual reality applications.
• We try a new ML algorithm which improves ad targeting for e-commerce companies, who run a special type of ad known as the "shoppable feed ad". We want to test if this new ML algorithm is better. How do we test it? How many ads, or ad viewers, or advertisers, do we need to collect data from before we can reach a statistically significant result?
• How would you recruit participants for interviews or focus groups, and what strategies would you use to ensure diverse perspectives are represented?
• If you have an experiment, but multiple hypothesis, what could go wrong? How do you control/correct for the potential pitfalls of multiple hypothesis testing?
• What's the novelty effect in A/B testing? How can it be identified and accounted for?

For more product experimentation practice, read our blog on 50 A/B Testing Interview Questions which also covers resources to learn this material in-case you haven't done much with product experimentation before. Also read pages 246-250 of Ace the Data Science Interview for a crash-course on the most popular A/B testing concepts that occur in interviews.

Meta Technical SQL Questions

Meta's technical skills round during the onsite interview is basically all about SQL. Just like the technical phone screen round, the exact flavor of SQL you use doesn't matter – it's not a test of nitty-gritty syntax. It's a test of how accurately and quickly can you translate a business question into a SQL query that gets the answer.

To get faster at SQL, try to solve at least 50 out of the 200+ FAANG SQL questions on DataLemur . Aim to solve a DataLemur SQL medium difficulty question in ~5 minutes, and a DataLemur Hard in ~10 minutes.

Here's a few example SQL interview questions from Meta:

Page With No Likes (Meta SQL Interview Question)

Assume you're given two tables containing data about Facebook Pages and their respective likes (as in "Like a Facebook Page").

Write a query to return the IDs of the Facebook pages that have zero likes. The output should be sorted in ascending order based on the page IDs.

Example Input:

Example Output:

The dataset you are querying against may have different input & output - this is just an example !

p.s. If you have literally no idea how to solve this, maybe give our free SQL tutorial a try first?

Weekly Churn Rates (Meta SQL Interview Question)

Facebook is analyzing its user signup data for June 2022. Write a query to generate the churn rate by week in June 2022. Output the week number (1, 2, 3, 4, ...) and the corresponding churn rate rounded to 2 decimal places.

For example, week number 1 represents the dates from 30 May to 5 Jun, and week 2 is from 6 Jun to 12 Jun.

Assumptions:

• If the last_login date is within 28 days of the signup_date, the user can be considered churned.
• If the last_login is more than 28 days after the signup date, the user didn't churn.

User ids 1001, 1002, and 1004 signed up in the first week of June 2022. Out of the 3 users, 1002 and 1004's last login is within 28 days from the signup date, hence they are churned users.

To calculate the churn rate, we take churned users divided by total users signup in the week. Hence 2 users / 3 users = 66.67%.

Want some more SQL Questions? Try these 9 Meta SQL Interview Questions .

Meta Data Science Behavioral Questions

These behavioral questions aim to assess your communication abilities and your decision-making when it comes to common Data Science conflicts & challenges you'll run into a workplace like Meta.

• Tell me about a time when you had to work on a challenging data science project. How did you approach the problem, and what was the outcome?
• Describe a situation where you had to communicate complex technical concepts to a non-technical audience. How did you ensure effective communication, and what was the result?
• Can you share an example of a time when you faced a setback or failure in a data science project? How did you handle it, and what did you learn from the experience?
• Discuss a project where you had to collaborate with cross-functional teams or stakeholders. How did you manage differing priorities and opinions, and what was the outcome of the project?
• Tell me about a time when you had to make a decision based on incomplete or ambiguous data. How did you approach the situation, and what were the implications of your decision?
• Give a time when you had to influence and push a stakeholder to make a decision that they don’t necessarily agree with, but the data supports.

For more insight into crafting kick-ass answers to behavioral questions, check out our Data Science Behavioral Interview Question Guide .

7 Best Resources for the Meta Data Science Interview

If you're serious about acing the Meta Data Science interview, one blog article ain't gonna cut it. Here's the 7 best resources to study:

• Cracking the PM Interview by Gayle Laakman McDowell : good for the Meta Product-Sense questions (Analytical Reasoning Round)
• Khan Academy Statistics and Probability Course : good for the Meta analytical execution questions that cover prob/stats
• DataLemur : 200+ SQL interview questions from Meta, and other big-tech companies like Amazon, Google, TikTok, Netflix etc.
• Ace the Data Science Interview : written by 2 Ex-Facebook employees, this is the go-to resource for Acing the Meta Data Science Interview. The book has 201 real FAANG interview questions, including 11 from Facebook/Meta.
• A/B testing Questions Blog : this guide walks you through how to run consumer experiments, which is a frequent topic due to how important product experimentation & interpreting test results is for Meta Product Analytics roles
• Meta ML/Engineering Blog : Get more familiar with the technical problems Meta's tackling.
• 1:1 Mock Meta Data Science Interview : Get 1:1 coaching with me (Nick Singh) – I'm ex-Meta and have helped 30+ land DS/DE/SWE offers at Meta!

6 Miscellaneous Tips to Ace Your Meta Interview:

• Think out loud 🤔: Provide a narrative as you go through the problem so that the interviewer has insight into your thought process. Don't freeze up!
• Deconstruct SQL problems 🛠️: Deconstruct complicated or ambiguous SQL interview questions into smaller groups, solve the sub-problems, and combine them for a final solution.
• Hints 💡: Pivot your answer if your interviewer prompts you that you’re heading in the wrong direction. Be open to the interviewer subtely guiding you if you go off track!
• Clarification 🔍: Ask clarifying questions during the interview, especially in product-sense questions.
• Say why you’re interested in a career at Meta 🌟: Meta interviewers like to see people who know about the company culture, products, and challenges. Don't admit to never using Facebook because it's for grandmas.
• Questions ❓: Ask questions about Meta and their sub-team IF there is time. But don't stress too much, Meta interviews are mostly decided by technical performance. And obviously don't ask about pay (it's good), work-life balance (it's bad), and how they like it (they have to lie).

Interview Questions

Career resources.

Meta Data Scientist Interview (questions, process, prep)

Data scientist interviews at Meta (formerly Facebook) are really challenging. The questions are difficult, specific to Meta, and cover a wide range of topics.

The good news is that the right preparation can   help you maximize your chances of landing a job offer, and we've put together the ultimate guide below to help you succeed.

Here's an overview of what we'll cover:

• Role and salary
• Process and timeline
• Technical skills
• Analytical execution
• Analytical reasoning
• Behavioral  
• Interviewing tips
• Preparation plan

Click here to practice 1-on-1 with data science ex-interviewers

1. meta data scientist role and salary ↑.

Before we cover your Meta data scientist interviews, let’s first look at the role itself.

1.1 What does a Meta Data Scientist do?

Data scientists at Meta are responsible for processing, analyzing, and interpreting data sets and using them to evaluate and make recommendations to improve Meta’s various products. They’re vital to the company’s optimization and decision-making process.

As a Meta data scientist, you’ll be integrated into what is known as a Meta Pod, which consists of software engineers, designers, product managers, data engineers, data analysts, and other functions depending on the product or service. It is your responsibility to use your analytics expertise to determine which opportunities to work on. You’ll be working closely with data engineers to gather data sets to extract insights from. You’ll also be the one to come up with the proper metrics to measure your team’s progress and meet your goals, in collaboration with the product team. You’ll likewise work closely with SWEs on experiments, from designing, monitoring, to analyzing them.

Because Meta is very product-oriented, data scientists are required to have good product sense. At the end of the day, as a Meta data scientist, your responsibility is to use your technical expertise to deliver insights that will help improve user experience on Meta’s products.

What skills are required to be a Meta Data Scientist?

Based on an analysis of the current data scientist posts at Meta , the minimum educational requirement for a data scientist depends on the specialization. Some posts will only require a Bachelor’s degree in Mathematics, Statistics, or a related technical field, while others may require a Master’s degree in a quantitative field like Computer Science, Economics, Engineering, Information Systems, Analytics, Mathematics, Physics, or Applied Sciences. Having at least 4 years of relevant work experience is a must.

Experience working on experimental design and using data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R) are also highly sought after. Excellent data visualization and stakeholder communication and presentation skills are also part of the minimum skills requirement. Some posts will require experience with predictive models.

If you have all these technical skills and have been able to use them to drive business decisions in a previous role, you’re a great candidate.

1.2 How much does a Meta Data Scientist make?

Based on the computations from Glassdoor data , the average data scientist salary at Meta is 37% higher than the estimated average salary of a data scientist in the US.

Location also plays a part in the difference in salary based on Glassdoor data. To compare:

• Meta India Data Scientist: est. average of $11kyear base pay
• Meta US Data Scientist: est. average of $175k/year base pay

Below you can see the average salary and compensation of the different data scientist levels at Meta US, as of early 2024, based on Levels.fyi .

While we presume that you already know which specific level you are applying for, it’s still good to double-check this with your recruiter. Your recruiter should be able to advise you on which level you’re being evaluated.

Ultimately, how you do in your interviews will help determine what you’ll be offered. That’s why hiring one of our ex-Meta interview coaches can provide such a significant return on investment.

And remember, compensation packages are always negotiable, even at Meta. So, if you do get an offer, don’t be afraid to ask for more. If you need help negotiating, consider booking one of our salary negotiation coaches to get expert advice.

2 . Meta Data Scientist Interview Process and Timeline ↑

2.1 what interviews to expect.

What's the Meta data scientist interview process and timeline ? It typically takes four to eight weeks and follows these steps:

• Resume screen
• Recruiter call (~15min). 
• Tech interviews (1-2 interviews, 45min each). 
• Onsite interviews (4 interviews, 30min each).

Let's look at each of these steps in more detail below:

2.1.1 Resume screen

First, recruiters will look at your resume and assess if your experience matches the open position. This is the most competitive step in the process, as millions of candidates do not make it past this stage.

If you’re looking for expert feedback on your resume, you can get input from our team of ex-Meta recruiters , who will cover what achievements to focus on (or ignore), how to fine-tune your bullet points, and more.

It can also be helpful to get an employee referral to the Meta recruiting team internally. This may not be possible, but if you do have a connection to someone who works at Meta, then this can help you get your foot in the door for an interview.

2.1.2 Recruiter phone screen

In most cases, you'll start your interview process with Meta by talking to an HR recruiter on the phone. But don't underestimate this initial interview. Although for many roles the initial phone screen is used to ask basic resume and behavioral interview questions, for Meta data scientists it often includes SQL and product analysis questions. So make sure you're ready from the beginning.

If you get past this first HR screen, the recruiter will then help you schedule the next round. One great thing about Meta is that they are very transparent about their recruiting process. And once you've been invited to the next round, they will likely give you some additional information about what to expect in their interview process.

2.1.3 Technical screen

The typical process is to just have one technical screen and then to advance to the onsite interviews. However, in some cases, candidates will have two technical screens before receiving their offer decision (i.e. there would be no onsite interviews in this case). If you're not sure which process applies to your role and location, then just wait and you should find out after you pass the recruiter phone screen.

The types of questions you'll be asked during the technical interview(s) are similar to the questions you'll encounter during the onsite interviews (see below). Based on this Data Science screen interview guide by Meta , you can expect questions in the following areas:

• Programming 
• Research Design 
• Determining Goals and Success Metrics 
• Data Analysis

These questions are meant to help the interviewer assess if you’re a good fit for the role, given your technical knowledge and skills, and previous job experience. The screen interview will last for 45 minutes.

2.1.4 Onsite interviews

The final stage in the interview process for Meta's data scientist candidates is the onsite interviews. As outlined by Meta's very useful onsite prep guide , the onsite typically includes 4 interviews of 30-45 minutes, consisting of:

• Technical skills . This is a coding interview where you'll analyze an open-ended product problem and try to solve it through code.
• Analytical execution . In this interview, you’ll be assessed on your ability to create hypotheses for launching new products and your knowledge of quantitative analysis.
• Analytical reasoning . In this interview, your research design, analytical design, data visualization, and storytelling through data will be evaluated.
• Behavioral. This is to test whether you’re a good fit for the company and the position, given your past experiences and your answers to hypothetical questions on what you might encounter at Meta.

For your technical skills interview, you'll need to work through your solutions on a whiteboard, or the online equivalent if you're not there in person. It's also worth mentioning that the questions you're asked in the onsite interviews tend to be more difficult than the questions from the technical round. So, be sure to double down on your preparation for them!

2.2 What happens behind the scenes

Throughout the interview process at Meta, the recruiter usually plays the role of "facilitator" and moves the process from one stage to the next. Here's an overview of what typically happens behind the scenes:

• After the technical interview, the interviewer(s) you've talked to submit their ratings and notes to the internal system. Your recruiter then reviews the feedback, and decides to move you to the onsite interviews or not depending on how well you've done.
• After the onsite, the interviewers will make a recommendation on hiring you or not and the recruiter compiles your "packet" (interview feedback, resume, referrals, etc.). If they think you can get the job, they will present your case at the next candidate review meeting.
• Candidate review meetings are used to assess all candidates who have recently finished their interview loops and are close to getting an offer. Your packet will be analyzed and possible concerns will be discussed. Your interviewers are invited to join your candidate review meeting, but will usually only attend if there's a strong disagreement in the grades you received (e.g. 2 no hires, 3 hires).  At the end of the candidate review meeting, a hire / no-hire recommendation is made for consideration by the hiring committee.
• The hiring committee includes senior leaders from across Meta. This step is usually a formality and the committee follows the recommendation of the candidate review meeting. The main focus is on fine-tuning the exact level and therefore the compensation you will be offered.

It's also important to note that hiring managers and people who refer you have little influence on the overall process. They can help you get an interview at the beginning, but that's about it.

3. Meta Data Scientist Example Questions ↑

Now we've covered the process, let's get into the type of questions you can expect for each type of interview. Bear in mind that the technical screen draws from the same question types as the onsite interviews, just in less depth.

Note that many of the questions below are asked in the form of case studies. To learn more, read our guide on data science case study interviews .

In the below sub-sections,  we've also compiled a selection of real Meta data scientist interview questions, according to data from Glassdoor . These are great example questions that you can use to start practicing for your interviews.

3.1 Technical analysis questions

Meta data scientists work with one of the strongest data sets in the world. They are expected to have fluency in SQL (or equivalent) and in this interview, you can expect mainly SQL-related problems.

You will be expected to work through your answers on a whiteboard (or online equivalent) and you should be well prepared to write SQL queries (with proper syntax).

In addition to SQL questions, you should also be ready for questions related to data structures, and algorithms, although these questions are less frequently asked (data scientists tend to have fewer engineering responsibilities at Meta than they do at other companies).

Finally, we recommend reading this guide on how to answer coding interview questions and practicing with this list of coding interview examples in addition to those listed below.

Meta data scientist interview question examples - Technical analysis

• Provided a table with user_id and the dates they visited the platform, find the top 100 users with the longest continuous streak of visiting the platform as of yesterday.
• Provided a table with page_id, event timestamp, and an on/off status flag, find the number of pages that are currently on.
• Given a database of posts and a database of comments on those posts, how do you determine how many conversations are happening in the comments per post on average?
• You're given two tables. One contains the date, post_id, relationship (e.g. friend, group, page), and interaction (e.g. like, share, etc.). The second table contains post_id, and the ID of the person who posted. How many likes were made on friend posts yesterday?
• What's the difference between a left join, a union, and a right join?
• Using SQL, how would you provide a distribution of rolling 7-day average money spent per person, broken up into categories of purchase?
• In SQL, how to combine two datasets while keeping all info?
• How can you pull the unique conversation events from a database in SQL?
• How to create a validation tool for Facebook Marketplace?

Data structure and algorithms

• There is an algorithm that rates posts on their likelihood of being spam. How would you check if the algorithm works?
• Given a list, search for consecutive numbers (n) whose sum is equal to a specific number (x).
• Given a list of people with things that they own, find the people who have common items and what they are.
• Can you find the first login for a platform, given a list of users?
• How do you revert a string?

3.2 Analytical execution

At the end of the day, Meta's data scientists help to drive product and business decisions. They need to be able to use their analytical skills to solve real-world business problems and contribute to the overall success of the team and company.

With that in mind, the analytical execution interview is meant to evaluate you on how you will use your hypotheses creation skills and your knowledge of core statistical concepts for data-driven problem-solving and other business decisions.

Prior to your interviews, you should take some time to brush up on statistics fundamentals and to practice giving concise explanations of statistical terms (e.g. p-value, recall, etc.). In addition, it's pretty common to get questions related to A/B testing, so if you have experience using A/B tests, we'd recommend preparing a specific example in advance.

It's worth noting that Meta says you won't face any specific machine learning questions, but if you have the relevant knowledge, you can weave it into your answer to deepen the discussion.

In this interview, you’ll be assessed on four key areas:

• Creating hypotheses: your ability to create hypotheses for launching new products and problem-solving; working knowledge of core statistical concepts: Law of Large Numbers, Central Limit Theorem, Linear Regression, Bayer’s Theorem
• Quantitative analysis: your ability to quantify a feature’s tradeoffs in terms of metrics
• Setting goals and success metrics: your ability to determine goals and create metrics
• Agility: your ability to adapt to data changes and challenges

Let's take a look at some questions.

Meta data scientist interview question examples - Analytical execution

• How would you measure the success of a product?
• What KPIs would you use to measure the success of the newsfeed?
• How would you improve notifications?
• Activity in Facebook user groups is down by 20%, what do you do?
• Friends acceptance rate decreases 15% after a new notifications system is launched - how would you investigate?
• The notification product will launch a new feature. The feature is a new type of notification. When your friends attend an event, you will get a notification. How do you measure the success of this new feature?
• Imagine a product similar to Facebook Marketplace called Facebook Restaurants. Measure the success of this new feature.
• How would you measure the success of a newly released feature that is similar to the Facebook group chat?
• How would you build a 'restaurants you may like' recommender system on the news feed?
• How would you predict churn rate?
• Given a table of data, how would you create a model to detect spam?
• How would you create a model to find bad sellers on FB Marketplace? How can you tell if your model is working?
• You see "average reels watched" has dropped precipitously suddenly, how would you figure out what's happening?

3.3 Analytical reasoning

For the analytical reasoning interview, you’ll be evaluated on how you structure ambiguous product questions and how well you design experiments to test hypotheses pinpointing the best data sets for specific product questions. You’ll also be assessed on your understanding of the downsides and biases of certain methodologies and how you plan to handle them. Lastly, your ability to extract relevant insights and tell a story through data will be tested as well.

Let's take a look at some example questions.

Meta data scientist interview question examples - Analytical reasoning

• How would you do an A/B test on your new metric to see if it truly captures meaningful social interactions better?
• Explain your process for doing A/B testing.
• How would you estimate how much fake news is on FB? How would you estimate its impact?
• How would you use data to confirm that users’ high school data is real?
• How would you evaluate the impact for teenagers when their parents join Facebook?
• How would you decide to launch or not if engagement within a specific cohort decreased while all the rest increased?
• How would you set up an experiment to understand feature change in Instagram stories?
• How would you determine the health of FB Groups?
• How would you determine if a new system that identified and banned accounts which were posting ads for prohibited content was working?
• Your PM is launching a new feature to improve the engagement on the newsfeed, how would you guide her on whether the overall impact is positive? How would you recommend setting up an experiment?

3.4 Behavioral questions

In addition to the question types outlined above, you can also expect to be asked some behavioral or "resume" questions about your past work experience, how you would react to hypothetical situations you might encounter at Meta, and your motivation for applying. Indirectly, these questions also evaluate your communication skills. 

Behavioral questions are a great opportunity to tell your story (in a concise way), and to demonstrate your alignment with Meta's values and culture. If you're applying directly to a job posting, you can also be strategic by aligning your answers for behavioral questions with the top qualifications that are listed in the job description. 

According to the Meta DS onsite interview guide , when answering behavioral questions, be sure to demonstrate how you:

• Operate in ambiguous and undefined projects.
• Move quickly and resourcefully.
• Can be open about your failures and talk through examples of what you’ve learned from them.
• Build relationships and collaborate with your direct and partnering teams to achieve mutual objectives.
• Influence and get buy-in from peers who may be resistant to your goals.
• Exhibit introspection and self-awareness.

Let's see some examples.

Meta data scientist interview questions - Behavioral

• Why data science?
• What do you do currently?
• Describe a data and analytics project you've worked on
• Tell us about your past experience, skills and interests
• What is your biggest weakness?
• What has been the biggest challenge you have taken on?
• Give a time when you had to influence a stakeholder to a decision they don’t necessarily agree with.
• Tell me about some work you are proud of.
• How would you handle ambiguity? How do you make recommendations in the face of ambiguity?
• What do you like about work, what do you dislike?
• Tell me about something challenging you are working on.
• Give an example of how you used your skill to gain insight into a difficult problem.

4. Meta Data Scientist Interviewing Tips ↑

You might be a fantastic data scientist, but unfortunately, that won’t necessarily be enough to ace your interviews at Meta. Interviewing is a skill in itself, that you need to learn.

Let’s look at some key tips to make sure you approach your interviews in the right way.  

4.1 Ask clarifying questions

Often the questions you’ll be asked will be quite ambiguous, so make sure you ask questions that can help you clarify and understand the problem. Most of the questions will focus on testing your technical proficiency.

4.2 Be conversational

Meta wants to know if you have excellent communication skills. So make sure you approach the interview like a conversation. 

Meta will also be testing you on your ability to tell a clear and concise story through data, especially to stakeholders who may or may not have a technical background. So be sure to brush up on your basics and practice interpreting them in a way that’s clear and easy for everyone to understand.

4.3 Think out loud

You need to walk your interviewer through your thought process before you actually start coding. Meta recommends that you talk even while coding as they want to know how you think. Your interviewer may also give you hints about whether you’re on the right track or not. Be alert for these, and be ready to pivot once you’ve gotten the prompt. This shows you’re eager to learn and listen well to feedback.

4.4 State and check assumptions

You need to explicitly state assumptions, explain why you’re making them, and check with your interviewer to see if those assumptions are reasonable. 

4.5 Present multiple possible solutions

Present multiple possible solutions if you can. Meta wants to know your reasoning for choosing a certain solution. 

When dealing with complicated or ambiguous product questions, show your ability to deconstruct such problems into groups, and demonstrate how you can combine these groups for your proposed solution.

4.6 Be honest and authentic

Be genuine in your responses. Meta interviewers appreciate authenticity and honesty. If you faced challenges or setbacks, discuss how you improved and learned from them. When talking about failure, don’t try to hide your mistakes or frame a weakness as a strength. Instead, show what you learned and how the failure helped you grow.

4.7 Center on Meta’s culture

Familiarize yourself with Meta’s core values and align your behavioral responses with them. Meta values certain attributes such as comfort with ambiguity, agility, collaborative nature, and a sense of urgency.

4.8 Brute force, then iterate

When coding, don’t necessarily go for the perfect solution straight away. Meta recommends that you first try and find a solution that works, then iterate to refine your answer.

4.9 Keep your code organized

Make sure to keep your code organized so your interviewer won’t have a hard time understanding what you’ve written. Meta wants to see that your code has captured the right logical structure.

5 . Preparation Plan ↑

Now that you know what questions to expect, let's focus on how to prepare.  Below is our four-step prep plan for Meta. If you're preparing for more companies than just Meta, then check our generic data science interview preparation guide .

5.1 Learn about Meta's culture 

Most candidates fail to do this. But before investing a ton of time preparing for an interview at Meta, you should make sure it's actually the right company for you.

Meta is prestigious and so it's tempting to assume that you should apply, without considering things more carefully. But, it's important to remember that the prestige of a job (by itself) won't make you happy in your day-to-day work. It's the type of work and the people you work with that will.

If you know data scientists, engineers , or PMs who work at Meta (or used to) it's a good idea to talk to them to understand what the culture is like.

Meta recommends checking out these resources to help you learn more about the company:

• Meta’s mission statement
• Meta's 6 core values  
• Meta Newsroom
• Meta Careers
• Meta Diversity
• Meta Employee Benefits
• Interviewing at Meta blog
• How to get users and grow (by Alex Schultz, VP of Growth at Meta)
• How Facebook Used Science And Empathy To Reach Two Billion Users (by FastCompany)

In addition, we would recommend reading the following:

• Facebook's hacker culture (by Mark Zuckerberg, via Wired)
• Meta annual reports and strategy presentations (by Meta)
• Meta's approach to tech trends (by CB Insights)
• Meta org culture analysis (by Panmore Institute)

5.2 Practice by yourself

As mentioned above, you'll encounter four main types of questions at Meta: technical skills, analytical execution, analytical reasoning, and behavioral. 

For the analytical execution and analytical reasoning interviews,  study our articles on how to crack product improvement questions and metric questions, as well as how to crack data science case studies . We created the product improvement and metric guides for product managers but you should find a lot of the content pretty helpful. We also recommend reading up on Meta's products , as it will help to be familiar with how they work.

We also recommend brushing up on statistics fundamentals for quantitative analysis questions which you will encounter in your analytical execution interview. Brilliant.org offers online courses designed around statistical probability and other useful topics, some of which are free. Search for specific questions and answers around statistics, machine learning, data analysis, and others on StackExchange .

For the technical analysis questions, your biggest priority should be to practice with example questions, especially the  SQL questions. To help with that, we'd recommend reading this analysis of the 3 "types" of SQL problems . 

Meta recommends these resources for your analytical and technical interview prep:

Analytical prep:

• How Experimentation Informs Product Development: LinkedIn
• The Pitfalls of A/B Testing in Social Networks
• Khan Academy Statistics & Probability Course
• Cracking the PM Interview by Gayle Laakmann McDowell

Technical prep:

• Mode Analytics SQL Tutorials
• Programmer Interview SQL Practice Database
• Python | SQL Comparison
• Data Transformations in R

For behavioral questions, we recommend reading our guide to Meta behavioral interview questions where you’ll find a step-by-step method. You can then use that method to practice answering the example behavioral questions provided in section 3.4 above.

Finally, a great way to practice all of these different types of questions is to interview yourself out loud. This may sound strange, but it will significantly improve the way you communicate your answers during an interview. Play the role of both the candidate and the interviewer, asking questions and answering them, just like two people would in an interview. Trust us, it works.

5.3 Practice with peers

Practicing by yourself will only take you so far. One of the main challenges of data scientist interviews at Meta is communicating your different answers in a way that's easy to understand.

As a result, we strongly recommend practicing with a peer interviewing you. If possible, a great place to start is to practice with friends. This can be especially helpful if your friend has experience with data scientist interviews, or is at least familiar with the process.

5.4 Practice with ex-interviewers

Finally, you should also try to practice data science mock interviews with expert ex-interviewers, as they’ll be able to give you much more accurate feedback than friends and peers.

If you know a data scientist or someone who has experience running interviews at Meta or another big tech company, then that's fantastic. But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can practice 1-on-1 with ex-interviewers from Meta and other leading tech companies. Learn more and start scheduling sessions today.

Related articles:

July 2023: AI Interviews at Amazon, Google, Meta, & Netflix - What to Expect

Since AI research and engineering roles (especially those people pursue after a Masters or PhD) are not as prevalent as SWE roles, the interview process is frequently misunderstood. Let's walk through what the interview processes look like at a few major tech companies and how they differ between Research Scientists, Applied Scientists, and Research Engineers. 

If you only read one sentence from this article, read this: 

Most research teams have leeway on how they conduct their interviews but you will almost always be asked to provide deep technical detail on your past research and field of research including what you think are the most pressing next hypotheses to research and how the landscape is evolving. 

Ok let's dive in :) 

Amazon has a lot of confusing role titles so let's go through them together. 

Applied Scientist

In short, Amazon Applied Scientists are Researchers and SWEs. 

You need to have the full abilities of a Research Scientist (almost everyone has a PhD) but also be capable of writing code and building solutions to your research. This is the highest paid position at Amazon and generally the most coveted. 

However, we would encourage you not to think of this as a traditional 'applied' role where the focus is often more internal research and code implementation. Applied Scientists have a large focus on publishing and working with the research community. It truly is a Research Scientist role except that you must also be capable of being a SWE as well when needed. 

Both Applied Scientist managers and Applied Scientists are paid higher than their Research Scientist or SWE counterparts. 

You can expect these to be some of the most challenging interviews at Amazon. You will have a set of research interviews where the interviewer will ask ML fundamentals as well as deep dive into your past research. Additionally, you should expect at least 1 Leetcode-style SWE interview.

Research Scientist

This is a pure research role. You are generally publishing and researching an area specific to your background and the team you’re on. You can expect all of the interviews for Amazon Research Scientist positions to be about your past research, your future research ambitions, and ML fundamentals. Expect the interviews to go deep on your area of expertise. Almost everyone who is an RS at Amazon has a PhD in Computer Science or Applied Mathematics.

Sometimes the interviewers will throw in a Leetcode question or two. Don't panic. They are just checking to see if you could alternatively be an Applied Scientist. We know people who have fallen flat here and still gotten excellent Research Scientist offers so don't let it scare you!

Data Scientist 

Data science at Amazon is rarely about research. In this role you can expect to be doing DS work that moves Amazon's products forward. Amazon Data Scientists do really awesome work but it's not research. 

Most DSs at Amazon do not have PhDs. If Applied Science and Research Scientist are like non-identical twins, DS is a distant cousin. 

Research Scientist or Applied Scientist?

Aren't sure whether you should interview as a Research Scientist or an Applied Scientist? If you feel like you can solve Leetcode medium (or ideally hard) questions and enjoy coding then we'd recommend applying for AS. If you hate coding or the idea of Leetcode is worse than eating an entire raw lemon then go for RS. 

Recruiters at Amazon are often siloed (i.e. they recruit for specific teams, not the full organization) so it's not uncommon for recruiters to try and force you into the AS or RS bucket based more on their hiring recs than your abilities. We recommend advocating to interview for the role that best fits your skillset and asking the recruiter to introduce you to those teams or recruiters who are responsible for recruiting for those teams. 

All Amazon interviews will include a bar raiser interview so if one of your interviewers is more senior or is being more challenging, that's normal.

Netflix Research , although filled with many smart, hard working scientists, rarely publishes because they focus more on using research internally.

Netflix Research Scientist interviews are comprehensive. You can expect a recruiter screen, several interviews on ML including problem framing and fundamentals, a coding screen, and generally a few interviews that are specific to your team (i.e. going deep on a specific research area). Additionally, expect conversations with the hiring manager to make sure you are a culture fit - culture is extremely important to Netflix so we recommend reading their guide to company culture beforehand.

The prized research role at Google is the Research Scientist role. 

From a compensation perspective, they are paid better than any other role at Google and generally you are focused on producing and publishing groundbreaking research. Google historically has a significant preference for their Research Scientists to have a PhD and publish papers at top conferences. 

The bar is high but generally people are humble and thoughtful. Being a researcher at Google is one of our favorite roles at the company because the community is strong and small - similar to what it was like to be a software engineer at Google ~10 years ago or what it's like to be part of the Principal Engineering community at Amazon. 

The team you join will have a large impact on your interview experience. We'd recommend interviewing with teams that align well with your past research and expect them to dive into it. Most Research Scientists at Google are expected to know how to code so you can generally expect two traditional Leetcode-style coding questions. The difficulty of those questions vary by team. 

If you have an engineering-heavy background (which is rare for this role) vs. a research-heavy background, expect the Leetcode problems to be hard - you will need to shine on your engineering (and still be strong in research). 

As with all interviews at Google you will have a Googly-ness interview to determine if you are a culture fit - just be nice, come with a few genuine questions, and have a good time. The easiest way to fail the Googly-ness interview is by not being a nice human - this interview isn't about your technical abilities so you don't need to brag/boast or try to be outstanding. 

Research Engineer

While they’re not common, you also may come across a job description for a Research Engineer position at Google. 

At Google Brain, Research Engineers have traditionally been allowed to flex between writing code and focusing on research. In contrast, at DeepMind and other parts of Google they’ve been focused more on ML engineering. 

Research Engineers typically have a Masters or PhD.

It's unclear as Google merges their divisions exactly how this role will be impacted. We'd highly recommend talking to current Research Engineers on the team you’re interviewing for to get a sense on what their % time split is between engineering and research so you can make sure this role is a fit for your interests and ask to interview for a Research Scientist position if not. 

Data Scientist

Similar to Amazon and Facebook, Google Data Scientists are a distinct role from researchers. They generally support product development instead of publishing research and have a mix of educational backgrounds from Bachelors to PhDs but a PhD is rarely a hard requirement. 

At a time when everyone in the industry is moving from open to closed source we love that Facebook Artificial Intelligence Research (FAIR) continues to publish creative, groundbreaking work. 

Yann LeCun is a brilliant but controversial leader and we personally like that he hasn't accepted that LLMs in their current transformer architecture are the be-all-end-all. Regardless of whether he is right or wrong we appreciate having different views in the community. 

It's worth distinguishing two confusing AI title differences at Facebook. There are Research Scientists within Facebook and then there are Research Scientists at FAIR. 

FAIR Research Scientists focus on producing groundbreaking research and publishing papers - they are the equivalent of Research Scientists at Google. 

Researchers at FAIR are given a lot of autonomy on what they want to work on. It's an excellent place to produce and publish in today's climate. In contrast, the work that a Research Scientist does outside of FAIR (but at Meta) varies widely. If you have a PhD and are doing engineering work you are often called a Research Scientist even though the work may not be research-focused at all. However, some RSs (outside of FAIR) do applied research for Facebook products.

Interviewing at FAIR

Assuming you want to be in a publishing role, here’s more information on interviewing with FAIR:

The on-site is similar to a faculty interview at an academic lab where you have a 'job talk' and several research-focused rounds. However, you can also expect to have multiple rounds of coding interviews which you need to pass. Sometimes a research and coding round will be merged into an 'in-domain' interview where they ask coding questions or more general questions that are related to your research domain. 

A final note on Meta - if you are interested in coding and machine learning infrastructure then a ML Engineer role is probably the right fit for you. It's very heavy on coding, pipelines, and systems but you are often working alongside a research team and supporting them so having some understanding of research is still valued. 

MLE interviews are very similar to SWE interviews but you can also expect to cover ML fundamentals.

The demand for AI researchers continues to grow, irrespective of layoffs and other business shifts. When we talk to leaders at FAANG and late stage startups there is immense pressure to incorporate AI (especially generative AI) into their products and that pressure comes from the board and investors. We don't expect demand for AI researchers to slow anytime soon. 

We've reached all-time highs in our negotiations for AI researchers in recent months at Facebook, Amazon, Google and Netflix ( here are 2023 benchmarks for researcher roles ) despite some companies attempting to try and be stingier given the market. 

However, research roles are changing in big tech as AI research becomes more closed source. We'd recommend interviewing at Facebook, Google, Amazon, and Netflix as even just being in the interview process with these companies can help you negotiate exceptional offers. You'll likely need to spend some time brushing up on your Leetcode and we'd also highly encourage you to prep answers for questions on your research and to read the latest, cutting-edge papers in your field. 

We've had 100s of clients join top AI labs so if you have questions about the interview process, what it's like to work at each lab, how to pick a team, or how to negotiate - just reach out to [email protected] for guidance :)

Jordan is a Lead Negotiator at Rora -- and the founder of pay equity startup 81cents, which helps historically-excluded minorities negotiate their pay through data collection and hands-on mentorship.

She's helped over 500 individuals negotiate for higher pay, better titles, and more significant roles and responsibility. Jordan's favorite negotiation was for a product manager who was able to increase her pay by 50%. The pay increase helped the candidate take care of a sick family member and pay for her wedding!

Jordan holds a BA from the University of Pennsylvania and an MBA from the University of California at Berkeley.

Over 1000 individuals have used Rora to negotiate more than $10M in pay increases at companies like Amazon, Google, Meta, hundreds of startups, as well as consulting firms such as Vanguard, Cornerstone, BCG, Bain, and McKinsey. Their work has been featured in Forbes, ABC News, The TODAY Show, and theSkimm.

1:1 Salary Negotiation Support

Step 1 is defining the strategy, which often starts by helping you create leverage for your negotiation (e.g. setting up conversations with FAANG recruiters).

Step 2 we decide on anchor numbers and target numbers with the goal of securing a top of band offer, based on our internal verified data sets.

Step 3 we create custom scripts for each of your calls, practice multiple 1:1 mock negotiations, and join your recruiter calls to guide you via chat.

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