Facebook is currently the largest social network, with a collection of products that help connect millions of people. The social media giant leverages oceans of data to perfect those products. For that reason, a Facebook data scientist plays a crucial role in driving business results for the company.
It’s estimated that Facebook generates about 4 petabytes (that’s 4,000 terabytes or 4 million gigabytes) every day. In a nutshell, the data scientists working there have to wrestle with all of that data, analyze it, and extract useful insights that drive important decisions.
Of course, there’s a lot more that goes into the job. If you’re considering becoming a Facebook data scientist, keep reading. In this article, we’ll talk about what they do, their typical roles and responsibilities, and how to become one.
Let’s get started.
What is a Facebook Data Scientist?
A Facebook data scientist is a professional who uses their statistical analysis, machine learning, programming, and data visualization skills to help Facebook product teams make better decisions and understand user behavior.
They basically influence the key decision-making that takes place regarding Facebook products. In a nutshell, they help:
- Product Operations – set goals for the product teams and measuring key metrics. Identify the potential causes of changes in metrics, monitor and critically evaluate key product metrics, and build dashboards along with writing reports.
- Exploratory analysis – Suggest new ideas to work on or new things to build based on user behavior. Develop a key understanding of ecosystems, user behaviors, and long-term trends. Construct logistic models to interpret user behaviors.
- Product Leadership – providing product teams a vision through the presentation of data-based recommendations. Also, spreading best practices amongst product teams and communicating experiment results to such teams are included under product leadership.
Facebook data scientists have significant experience in managing large data sets, quantitative analysis, and communicating their findings to stakeholders.
The research efforts of a Facebook data scientist span a plethora of disciplines, such as computational social science, econometrics, operations research, market intelligence, survey science, and statistical computing.
What Does a Facebook Data Scientist do?
Data scientists try to employ a blend of methods to achieve their goals, including machine learning, field experiments, surveys, and information visualization. These individuals also construct scalable platforms for collection management, data analysis, and contribute to the key business strategy.
As far as a Facebook data scientist is considered, being able to do quantitative analysis of data sets, knowing coding languages, such as SQL and Python are the mere prerequisites.
These professionals get the opportunity to work on a variety of product areas.
They perform data analysis to understand the behaviors of users of different Facebook products, like WhatsApp and Messenger. They then work with the product leadership to figure out potential problems and help the company achieve its objectives by improving user engagement, growing revenue, and increasing social media reach.
In addition, data scientists at Facebook try to improve the user experience, make their apps more intuitive, and figure out ways to facilitate meaningful social interactions.
These data scientists have to work along with data analysts at Facebook who develop roadmaps based on the current state of data forms and generation environments.
Key Duties and Responsibilities of a Facebook Data Scientist
Here are some of the key duties and responsibilities of a Facebook data scientist:
- Use quantitative analysis techniques, data mining, and data visualization to understand how users interact with both core and business products of Facebook.
- Team with the product and engineering teams to identify trends and opportunities, perform critical analysis, and solve business problems. This also involves setting team goals and working with cross-functional partners to guide the product roadmap.
- Inform, support, influence, and execute the product decisions and product launches.
- Explore, analyze, and aggregate large data sets to provide meaningful information and create intuitive visualizations to communicate those results to a broad audience.
- Design informative experiments by taking into account various statistical measures, sources of bias, target populations, and potential for positive results.
- Coordinate with engineers on logging, product health supervision, and experiment design/analysis.
The exact day-to-day may vary, but the aforementioned points pretty much cover everything that a Facebook data scientist does.
How to Become a Facebook Data Scientist? [Eligibility Criteria]
Here are some prerequisites for applicants:
- Academic Requirements – the candidate must have done BS/BA in Computer Science, Mathematics, or other related technical fields including relevant boot camps.
Technical Expertise – the applicant must be proficient in Python or Java. In addition, their data analyst skills should include artificial intelligence and machine learning algorithm design.
- Professional Experience – the person applying for the role must have at least two years of hands-on experience in the data warehouse space as well as writing SQL query statements and schema design. Also, the individual must have a minimum of three years of experience with OOP Programming language, ETL design, and working with MapReduce or MPP systems.
In addition, the applicant must have experience in identifying deliverables, gaps, and inconsistencies in product-related or market data. Lastly, they must have the experience to communicate confidential data with internal clients and handle their queries well.
How is a Facebook Data Scientist Recruited?
If you make it through the initial assessment, you get called for an interview with a Facebook recruiter. Before calling you for the interview, these recruiters will visit your LinkedIn profile to verify the details listed on your resume.
Below, we’ve broken down the entire process:
It’s natural for one to be nervous, but the best way to prepare for the interview process is to know what sort of interview questions will be asked.
The Facebook data scientist interview is mostly straightforward. The recruiter will typically reach out to you through email, LinkedIn, or the website.
These interview questions will typically inquire about your years of experience, whether you know coding languages, such as SQL and Python, and if you can perform in-depth quantitative data analysis with the information collected.
You should be able to answer these interview questions if you know your stuff. The interview process is broken down into the following stages:
the recruiter talks to you over the phone to find out about your objectives, interest in the company, and which department you will fit in best. It usually lasts for 30 minutes and the main objective is to check if your expectations align with Facebook’s vision.
There is a high chance that the recruiters might transfer you to another hiring manager recruiting machine learning engineers or growth marketing analysts if your skillset is better suited for those roles.
Some candidates are introverts and they might find the data science role to be more business-facing than they would prefer. Therefore, the applicant can be considered for multiple positions at Facebook, such as a data analyst or product analyst.
The second stage revolves around product analytics questions. Candidates are usually asked product-specific questions, such as:
- How would you improve notifications?
- How would you measure the success of a new product?
- What metrics will you use?
The purpose of these questions is to test the candidate’s understanding of different products and what algorithms they will use to approach problem-solving.
The third stage of the interview is conducted on-site. It inquires you about your technical knowledge.
You will be expected to show your proficiency with SQL and Python to solve multiple business problems and model algorithms for artificial intelligence.
These onsite interviews are quite challenging because they include questions related to SQL, product interpretation, quantitative analysis, and an applied data. In the SQL technical question, you will be given a data set and asked to solve problems using SQL. However, this SQL question is quite lengthy, confusing, and requires a longer solution as compared to one in the technical screening stage.
Product interpretation questions will typically ask you to measure product performance with details like KPIs (Key Performance Indicators) and implement b testing or a/b testing. If you are lucky, you might simply be asked this. However, if the interviewers feel like examining you to the core, they might ask you to create a high-level plan of the implementation.
The quantitative analysis question is a typical statistic problem that tests your basic understanding of statistical data analysis. Most candidates find this to be the easiest part of the interview as it simply examines the knowledge that you must know fundamentally when applying for the role.
The applied data questions will require you to consider a solution at a high level. You will be asked to mention your process, list any assumptions, discuss any possible lags or shortcomings, and how you have prepared to deal with them.
Lastly, you will have to explain to the interviewer how you concluded. You might be asked some follow-up questions during this time to check how critically you are thinking about the solutions. Some examples of vague questions asked here are discussed below:
- Do people interact more or less on Facebook with their families?
- What tools will you use to measure these interactions?
- How will you determine if people are siblings?
- How can Facebook use this information for the better?
- What factors will you take into account to discern users?
- How does an activity differ depending on the season? Is a comment worth more than a like?
Based on your technical expertise, the recruiters will decide which data science role will suit you best, whether you will be able to identify business problems and solutions and inform the development of new products and services. This final stage is typically held at either the Menlo Park, Seattle, or the New York Facebook campus.
To ace the interview, you ought to practice a dozen of hands-on problems and a comprehensive data science project. Data scientists who were able to clear the interview and get a good role at Facebook have prepared Educative’s text-based courses to help you know what to expect on the exact day of the interview.
What types of Data Scientists does Facebook hire?
Here’s a list of the different type of data scientists that Facebook typically hires:
- Financial Data Scientists – these people make sure that Facebook is managing its finances accurately, i.e. they check if the money is being earned and spent according to the business plan. Financial Data Scientists help build models to understand what revenue and expenses will look like in the time to come or near future. Managing this role takes a lot of time and effort.
- Product Data Scientists – they play a crucial role in successfully delivering a quality user experience. They work on understanding how users interact with multiple Facebook products and analyze to help improve products and user experience.
- Community Operations & Business Integrity Data Scientists – these people ensure that Facebook’s content, such as Ads, Facebook posts, Marketplace posts, etc. are user-friendly and compliant with Facebook’s policies. These data scientists work in teams with Data Engineers to build or improve the machine learning models that can proactively detect any non-compliant content so that corrective actions can be taken immediately.
Consider the area you’d be interested in before applying.
Wrapping it Up
In this technologically advanced world, you need someone smart to effectively manage your company’s data. And when you’re the world’s largest social network, you definitely need the cream of the crop.
A Facebook data scientist brings a strong educational background, along with an equally strong business acumen to the table. As mentioned earlier, they have to wrestle with large data sets and drive critical business decisions.
At Facebook, collaboration matters. The data scientists working there have to dig deeper into data sets and work with a team of data engineers and technicians to find ways for the company to improve its tech, products, services, and overall performance.
Furthermore, Facebook, unlike other companies like Amazon, looks for data scientists with excellent communication skills. Therefore, they must be able to convey their messages properly because an inept explanation of different scenarios will land the data scientists in a compromising position.