Interview Guide Jun 14
Jun 143 rounds
Are you interested in applying for data scientist roles in Meta? Here's a detailed guide containing interview questions and tips to help you out!
At Meta, Data Scientists work to improve products they build across Meta’s entire family of applications (Facebook, Instagram, Messenger, WhatsApp, Oculus). By applying your technical skills, analytical mindset, and product intuition, you will help define the experiences for billions of people and hundreds of millions of businesses around the world.
You will collaborate on a wide array of product and business problems with a diverse set of cross-functional partners across Product, Engineering, Research, Data Engineering, Marketing, Sales, Finance and others. You will use data and analysis to identify and solve product development’s biggest challenges. You will influence product strategy and investment decisions with data, be focused on impact, and collaborate with other teams. By joining Meta, you will become part of an analytics community dedicated to skill development and career growth in analytics and beyond.
Meta works in various domains, including social media, advertising, e-commerce, and augmented reality. For example, they were recently looking for a DS for their Mobile Identity Team. The Mobile Identity team builds Meta's authentication layer, used to identify users from all across Meta's family of apps, using new and sophisticated authentication methods. As a Data Scientist on the team, you will guide their product in identifying opportunities to improve the authentication experience for billions of users and work closely with cross-functional teams to deploy those solutions.
Meta ends up hiring not only Data Scientists but also several roles ancillary to this role, such as Machine Learning Engineers and Research Scientists who often work on overlapping but more specialized topics.
How to Apply for a Data Scientist Job at Meta?
To apply for a Data Scientist job at Meta, browse the job listings on Meta's career website and find the data scientist position that best matches your qualifications and experience. Once you have found a position that you are interested in, you will be able to submit an application online. However, we would highly recommend taking the referral route if you know someone in the company as it increases your chances meaningfully. One tip regarding your resume - make a few tweaks for the position and the role you are applying for which will help you have a better chance compared to other candidates. If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters will give you feedback on your resume.
It's important to note that the application process may vary depending on the position and location, so you should always check the specific job listing for more information on the application process.
As a part of the Meta Data Scientist interview, you will need to go through multiple interview rounds:
1. Recruiter Screening - The recruiter screening will be a conversation with a recruiter, detailing your DS background, your past relevant projects, and a quick assessment of your skill sets based on your resume.
2. Initial Screening - During this round, you will be asked a series of behavioral questions, followed by a case study that includes a SQL-based challenge.
3. On-site Round - The final stage of the interview process will be an on-site interview comprising multiple different interview rounds - Technical Skills Round, Analytical Execution Round, Analytical Reasoning Round and Behavioral Round.
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The recruiter screening will be a conversation with a recruiter, detailing your DS background, your past relevant projects, and a quick assessment of your skill sets based on your resume.They will ask you about your areas of expertise and experience with relevant programming languages, frameworks, and tools. The recruiter will also ask you to provide examples of projects you have worked on, and how you have addressed technical challenges in those projects. This round is designed to assess your technical skills and suitability for the DS role at Meta.
- Why do you want to join Meta?
- Why do you think you will be a good fit for the role?
- What responsibilities do you expect to have from your job at Meta?
- What makes you the best candidate for this position?
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During this round, you will be asked a series of behavioral questions, followed by a case study that includes a SQL-based challenge. The case study primarily involves product-based questions that require candidates to provide KPIs and data to support their answers. This process is designed to assess a candidate's problem-solving skills, data analysis capabilities, and their ability to communicate their insights effectively.
The interviewer will also pay close attention to your previous experience in the field. This will likely involve questions about the types of projects you have worked on in the past, the types of data sets you have analyzed, and the techniques you have used to analyze them.
Note that for DSes in different teams at Meta, the initial screening can differ and can be specific to that role and that team.
- Imagine you are working on a product team that wants to understand user engagement with a new feature. What SQL queries would you use to analyze user activity and engagement with the feature?
- Given a database schema and data, write a SQL query to extract specific information required to answer a given product-based question.
- How would you go about analyzing user engagement with a new feature and identifying areas for improvement?
- If you were tasked with improving the performance of a certain product, what KPIs would you track and how would you go about improving them?
- Can you describe a particularly challenging data analysis project you worked on in the past? What were some of the key challenges you faced and how did you overcome them?
- What types of data sets have you worked with in the past? Can you give examples of the size and complexity of these data sets?
- What data analysis techniques or tools are you particularly skilled in? Can you give examples of how you have used them in previous projects?
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The final stage of the interview process will be an on-site interview comprising multiple different interview rounds. Some of the example rounds are:
- Technical Skills Round: In this interview, you’re going to be tested on skills revolving around your programming and data analysis abilities as well as how well you’re able to narrow down on goals and success metrics for products. You’ll typically receive an open-ended problem and you’ll be expected to structure an approach around how you’d leverage data to solve it. This will involve executing solutions within the interview, and SQL is the most commonly used language by candidates for this section. Don’t forget - your interviewer might provide new information or constraints as you’ll go along, so you’ll need to keep adapting.
- Analytical Execution Round: In this round, the questions will focus on understanding hypotheses for launching new features, quantifying tradeoffs of a feature in terms of metrics, and applying basic statistics concepts like mean/expected value, median, mode, percentiles, and common distributions.
- Analytical Reasoning Round: This round evaluates research design, analytical design, storytelling through data, and data visualization. You will be asked to frame ambiguous product questions, design experiments to test hypotheses, and identify biases in the analysis or experiment.
- Behavioral Round: In this round, you will be asked hypothetical questions on things you might encounter at Meta, as well as behavioral questions that draw from your previous experience.
Please note that these are just some of the examples of the rounds you could face. However, the rounds can be different for different roles and positions.
- How would you approach a problem where you have a large dataset and need to find the most common values in a specific column?
- Write a function that takes in a list of integers and returns the sum of all the even numbers in the list.
- Given a string, write a function that determines whether it is a palindrome.
- How would you determine whether a new feature that you launched had a positive impact on user engagement?
- If you had to choose between two different metrics to measure the success of a feature, which metric would you choose and why?
- If you were given data on user engagement for a new feature, how would you determine whether the engagement levels were statistically significant?
- How would you design an experiment to determine whether users prefer a new feature over an existing one?
- What are some potential biases that could occur in a study measuring the effectiveness of a new feature, and how would you account for them?
- Given a data visualization, how would you interpret the findings and communicate them to a non-technical audience?
- How do you approach working with a team to solve a difficult problem?
- Describe a time when you had to adapt to a new situation or unexpected challenge at work.
- How do you ensure that all team members feel included and valued in a project?
When you are preparing for a Meta Data Science interview - we’d recommend the following things to keep in mind:
- Have concrete examples, and use the S.T.A.R. method to mentally organize your thoughts. This will provoke a well-thought-out and chronological action of events.
- As you answer your interviewer’s questions, ask yourself if your responses include examples that show how you:
- 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.
The responsibilities of a data scientist at Meta across roles can broadly be seen as-
- Utilize advanced analytical and statistical techniques to solve complex problems with large data sets. For example, using machine learning algorithms to identify patterns in user behavior to improve product recommendations.
- Develop data-driven strategies for product development and improvement based on quantitative analysis, experimentation, and data mining. Analyzing user engagement metrics to identify areas for product improvement and developing strategies to address those areas.
- Set goals, forecast performance, and monitor key product metrics to evaluate the success of product efforts and identify areas for improvement. For instance, establishing metrics for a new product feature and monitoring them to assess its impact on user engagement and revenue.
- Drive product roadmaps through insights and recommendations based on data analysis and identify user pain points through data analysis and recommending features to address those pain points.
- Identify opportunities to improve existing distributed systems and machine learning stacks. For instance, developing a more efficient algorithm for processing and analyzing large data sets to improve the performance of a recommendation system.
Here are some of the skills and qualifications that may be required for a Data Scientist at Meta.
- When applying for roles in ML Modeling, Ranking, Recommendations, or Personalization systems, ensure you have a minimum of 8 years of experience in one or more of these areas. PhD students can usually get away with lower years of experience.
- You’ll need to be super competent in data querying languages, such as SQL, and scripting languages like Python or R, as well as statistical/mathematical software like R.
- You should ideally have at least some past experience with statistical data analysis, including linear models, multivariate analysis, stochastic models, and sampling methods.
- If you’ve previously worked in the ML ecosystem and are familiar with applying machine learning techniques to big data systems such as Spark and Hadoop, particularly with TB to PB scale datasets - this can really help you stand out.
It's important to keep in mind that this list is not exhaustive, and the requirements and qualifications may vary depending on the position and location.
The salary of a Meta 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.
The interview process for a Data Scientist role at Meta typically includes 3 primary rounds - a recruiter screen, followed by the initial screening, and an onsite interview. The recruiter screening will be a conversation with a recruiter, detailing your DS background, your past relevant projects, and a quick assessment of your skill sets based on your resume. During the initial screening round, candidates will be asked a series of behavioral questions, followed by a case study that includes a SQL-based challenge. The final stage of the interview process will be an on-site interview comprising multiple different interview rounds - DS Technical Skills, DS Analytical Execution, DS Analytical Reasoning and DS Behavioral.
What is the role of a data scientist?
A data scientist is responsible for extracting insights and valuable information from large sets of data to aid in decision-making processes.
What should I expect in a data scientist interview?
- Technical questions assessing your programming, statistical, and mathematical skills
- Case studies or data analysis exercises to demonstrate your problem-solving abilities
- Behavioral questions to evaluate your communication and teamwork skills
- Questions about your experience with specific tools or techniques
What skills are required to become a data scientist?
- Proficiency in programming languages such as Python or R
- Strong knowledge of statistics and mathematics
- Experience with data analysis and visualization tools
- Understanding of machine learning algorithms and techniques
- Excellent problem-solving and communication skills