Facebook Data Scientist

Difficultyhard

The role of a Facebook Data Scientist

The data scientist role at Facebook combines strong analytical and technical skills with sharp product sense. Compared to other big tech companies, the data scientist role here is a broad one and involves setting team goals, finding opportunities in the data to shift product focus, modeling predictions, and setting a culture of rigorous experimental testing. The role focuses more on deploying your data science understanding and quantitative skills to optimize Facebook's products and adding business value to them. Here's the role in a little more detail.

  • Applying your knowledge and skills in quantitative analysis, data mining, and the presentation of data to get a clear idea of how Facebook’s users interact with both their consumer and business products.
  • Using quantitative tools to see through opportunities, set team goals, and work with cross-functional partners to guide the product development/improvement roadmap.
  • Informing, influencing, supporting, and executing their product decisions and product launches
  • Forecasting and setting the product team’s goals
  • Exploring, analyzing and aggregating large data sets to provide actionable information, and creating intuitive visualizations to convey those results to a broad audience.
  • Designing informative experiments considering statistical significance, sources of bias, target 
    populations, and potential for positive results.
  • Collaborating with engineers on logging, product health monitoring, and experimenting with design/analysis
  • Working in Hadoop and Hive primarily, sometimes MySQL, Oracle, and Vertica.

Skills/Qualifications required/preferred for Facebook data scientist 

  • 2+ years of experience doing quantitative analysis within a large-scale company or fast-paced environment
  • Experience in SQL or other programming languages
  • Development experience in any scripting language (PHP, Python, Perl, etc.)
  • Experience communicating the results of analyses with product and leadership teams to influence the strategy of the product
  • Knowledge of statistics (e.g. hypothesis testing, regressions)
  • Experience manipulating data sets through statistical software (ex. R, SAS) or other methods

Interview Guide

The interview process for the Facebook data scientist role consists of 2 stages:

  • Initial Screening round (45 minutes)
  • Onsite interview

The initial interview will be a 45-minute video conference with a Facebook data scientist. The interview will include these sections:

  • Analytical: 10 – 20 minutes.
  • Technical: 10 – 20 minutes.
  • Q&A: 5 minutes.

Initial Screening round

Overview

The initial screening round is 45 minutes duration. It has three main sections:-

Analytical Section

The analytical section takes about 10-20 minutes of your initial screening interview time. This part of your initial screen is designed to help the interviewer assess your product sense. The questions asked will allow you to show the interviewer your approach to solving business questions and problems, as well as how creative and articulate you are at thinking through these problems while solving them. Do keep in mind that it’s not about arriving at the perfect or correct answer, but rather about your approach to the problem.

To prepare, spend some time engaging with Facebook Products less as a user and more as someone who is tasked with improving or developing these products(such as Ads, Mobile, Timeline, News Feed, Messaging, etc).

Technical Section

Like the analytical section, the technical section too lasts for 10-20 minutes. During the technical section of the interview, the interviewer will be assessing your ability to translate a high-level question into an execution strategy and explain how the result is relevant and what aspects may still be lacking.

What the interviewer will assess

  1. Language-neutral skills in coding/data manipulation
  2. Working with grouping and aggregate functions.
  3.  Utilizing different types of joins (left, inner, outer, etc.) including when and how to use a self-join.
  4. Appending multiple data sources (union in SQL, concat in Pandas, bind_rows in R).
  5. Filtering data by multiple, complex conditions.
  6. De-duplicating, sorting, handling missing/incomplete data.
  7. Assessing Efficiency. The interviewer may ask you to think of more efficient ideas or to explain why you’re making certain efficiency/simplicity tradeoffs.

SQL Skills

  • You may work in whatever dialect you like, but you’ll be able to answer all questions with ANSI-standard functions (think PostgreSQL). If you use a dialect-specific syntax, you may need to explain it to your interviewer.
  • Try to maintain consistency in capitalization/indentation style for better readability.

 Python/R Knowledge

  • Given the heavy focus on data manipulation, most people choose to use libraries, such as Pandas / NumPy in Python or dplyr in R. It’s possible to solve the questions in pure Python / R (or any Turing-complete language), but doing so will likely be much slower and more difficult.
  • The interview will either be on a whiteboard or in a plain text environment, so there’ll be no access to function autocomplete or help documentation.
  •  A few small mistakes in syntax won’t automatically disqualify you, but pseudocode or a general explanation isn’t acceptable. You must know the function names, input arguments, etc., to implement the core skills listed above.

Tips

  1. Think out loud.
    Narrate your approach to the problem/question asked as you go through the problem so that the interviewer has insight into your thought process.
  2. Deconstruct problems. 
    Follow the modular thinking approach to big ambiguous problems, breaking them into smaller groups, and combining the groups for a solution.
  3. Hints.
    Resort to mid answer course correction if your interviewer prompts you that you’re heading in the wrong direction.
  4. Clarification.
    Ask clarifying questions during the interview.
  5. Prepare an answer to the cliched "Why Facebook?" question.
    Facebook interviewers like to see people who know about the company's environment, projects, challenges, etc.
  6. Questions. 
    If time permits you may pop in a few questions yourself, say about Facebook and analytics.

Always remember to keep your Product Owner hat on. Think like a member of the product team that built the product/feature for both parts of your interview. The interviewer must feel that you’re thinking about these questions:

  • Why do you think they made certain decisions about how it works?
  • What could you do to improve the product?
  • What kind of metrics would you want to consider when solving questions around a product’s health, growth, or engagement?
  • How would you measure the success of different parts of the product?
  • What metrics would you assess when trying to solve business problems related to our products?
  • How would you tell if a product is performing well or not?
  • How would you set up an experiment to evaluate any new products

QnA Round:

The QnA round consists of a few questions of general nature that the recruiter might put to you or vice-versa. This is a short session of roughly 5 minutes.

Interview Questions

Sample Questions

Knowing the following:

  •  An attendance log for every student in a school district with: attendance_events: date | student_id | attendance
  • A summary table with demographics for each student in the district: all_students : student_id | school_id | grade_level | date_of_birth | hometown

Using this data, how would you answer the following? 

  • What per cent of students attend school on their birthday?
  • Which grade level had the largest drop in attendance between yesterday and today?

Onsite interview

Overview

Onsite interview

The initial screen is followed by an onsite round. The onsite interview will test more deeply the concepts and skills tested in the initial screener. Also, questions from another focus area, quantitative analysis, will be asked. Throughout your discussions during the day, the interviewers will be judging you on your ability to tell a compelling story with data, make data-driven decisions, and impact change through product development and optimization. Many of the questions throughout the interviews will be in the context of Facebook's products. Therefore it would be worth preparing for the different metrics that you’d use to measure the success of different Facebook Products for the interview loop.

Structure of the onsite interview:

The onsite interview will include the following 30-minute sections: 

  • Analysis Case: Product Interpretation.
  • Analysis Case: Applied Data.
  • Quantitative Analysis.
  • Technical Analysis.

Analysis case 1: Product Interpretation

The product case study is focused on understanding user behaviour through data and metrics. Product interpretation (PI) is all about translating user behaviour into product ideas and insights using data and metrics. 

What the interviewer is looking to assess:

  • Understanding of  hypotheses for launching new features: “How can I improve a product?”
  • Ability to consider and quantify tradeoffs of a feature in terms of metrics.
  • Ability to design experiments to test these hypotheses.
  • How you interpret the results of experiments.
  • How you communicate decision-making via metrics.

Sample Questions:

How would you evaluate YouTube’s video recommendations?

How would you make facebook's newsfeed feature more relevant to a particular age group?

How would you measure the performance of Facebook Ads?

Analysis case 2: Applied data

The applied data interview primarily tests the technical side of your problem-solving approach using data. To perform well in this section, it's helpful to engage with each of facebook's core products, trying to reverse-engineer in your mind how 

these products came to be, what metrics, and what testing, hypotheses and experimentation were used.

Applied data questions will require you to:

• Consider what data sets are best suited to answer a product question.

• Draw inferences from a data set.

• Combine multiple signals into a data-informed statement.

• Map analytical insights back to product impact.

Sample questions

Do people interact more or less on Facebook with their siblings?

How would you measure social interaction?

How does activity vary depending on the season? What region/regions are you considering? 

How would you weigh a user's Facebook activity? Does a comment carry more value than alike?

What factors would you use to distinguish users?

How could this information be of use to Facebook?

Quantitative analysis

This part of the interview focuses on basic questions designed to evaluate your quantitative reasoning and applied statistics skills. It would be very helpful to brush up on the core stats concepts that you might use to solve business problems.

It has 2 parts:

  1. Quantitative reasoning
  2. Applied Statistics

Quantitative reasoning:

Key concepts/skills tested:

  • Knowledge of key mathematical concepts such as probability
  • Statistical knowledge
  • How these concepts relate to Facebook products

Sample questions:

  • What do you think the distribution of time spent per day on Facebook looks like? 
  • What metrics would you use to describe that distribution?”

Applied Statistics:

This is hands-on skill testing. Here the focus is on the practical application of statistical concepts.

Key skills tested:

  • Whether you can apply statistical concepts to real-world problems
  • Whether you can draw meaningful and logical inferences from data using core statistical concepts.

Questions will most likely cover the following concepts/areas:

  • Estimation and logical reasoning in the context of a real-world product.
  • Elements of descriptive statistics (mean/expected value, median, mode, percentiles, etc.).
  • Common distributions, such as binomial or normal distributions.
  • The profile of real-world data.
  • Law of Large Numbers, Central Limit Theorem, Linear Regression.
  • Conditional probabilities, including Bayes’ Theorem.

Sample questions:

  • How do you apply A / B testing? 
  • Do you know how to interpret experiment results
  • Do you understand common distributions?

Technical analysis

The technical analysis section of the onsite loop is similar to the technical section of the initial screen. Here, the interviewer assesses your ability to brainstorm given data, and analyze open-ended product-related problems with code. 

Skills tested:

  • Ability to structure and articulate a solution based on data while solving an open-ended problem.
  • Using your coding skills to reach an executable solution based on a well-defined approach.
  • Identifying and addressing edge cases.
  • Adapting or improvising code based on new information and/or constraint

Sample questions:

  • Given the timestamps of logins, how many people on Facebook were active all seven days of a week on a mobile phone?
  • How do you determine what product on Facebook was used most by the non-employee users for the last quarter?

Tips for the technical analysis interview:

  • Most questions are designed/based on SQL, so proficiency in SQL will be an added advantage.
  • Interviewers will be asking whiteboarding solutions, so make sure you practice a few problems on the whiteboard before your interview.
  • While minor syntax errors in coding may not be penalized, you must be able to articulate your logic and approach in the code to the interviewer.

When you're ready for some *real* feedback - book a mock interview with a Facebook Data Scientist.

Book session here

Salary ranges for Facebook Data Scientists

The salary of a Facebook Data Scientist varies widely. It usually starts at around 200,000 USD (total compensation) for a starting level role, and goes up to 350,000 USD (total compensation) for senior level roles. The median salary hovers around the 230,000 USD mark (total compensation) for someone with about 1 years of experience. The breakdown of this is about 151,000 USD in base salary, about 65,000 USD in stock, and a 15,000 USD bonus component.

Frequently Asked Questions

How many rounds are there in the Facebook Data Science interview?

There are two rounds, namely the Initial Screening Round and the Onsite Round.

Does the the Initial Screening round have any subsections?

Yes, the Initial Screening round has 3 subsections, namely Analytical Section, Technical Section and a QnA round.

How long does the onsite round last?

The onsite round is the lengthiest of all the interview rounds lasting for about two hours.

What is the structure of the onsite round?

The onsite round has four 30-minute sections, namely Analysis Case: Product Interpretation, Analysis Case: Applied Data, Quantitative Analysis, and Technical Analysis.

Guides