Google Data Scientist Interview Guide

tl;dr

Google began as a research project in the mid-90s, and has since grown into one of the largest and most influential technology companies in the world. Although it began as a search engine, Google’s highly innovative set of data-based products allowed the company to expand into a huge range of different offerings, including email services, video streaming, digital advertising, smartphones, and large language models. Nearly everything they do is informed by Google’s trove of user and customer data, which makes data science a central component of their competitive advantage.

Google’s current headcount sits at around 183,000 full-time employees. Their job interview process is notoriously involved, and has been refined over nearly two decades to help identify the candidates who best fit Google’s cultural values and the needs of the hiring team. Although they have a defined philosophy, sometimes referred to as “Googliness,” it’s worth noting that culture fit isn’t as much of a priority as it might be at other FAANG organizations. Because each team has a good amount of discretion and control over the hiring process, the focus during interviews is team-dependent and may change significantly from role to role.

One thing that clearly defines Google’s approach to nearly everything is their use of data to make highly informed choices. Collecting and analyzing user data, identifying patterns, and taking steps to make sure this data is both secure and usable is central to their organization. If you’re interested in working on products and tools with a global reach, solving complex problems involving enormous datasets, and you like working cross-functionally with engineers, product managers, and other stakeholders, you might be a good fit for a data science role at Google.

What does a Google Data Scientist do?

Because Google offers such a wide range of products, hardware and software tools, open source contributions, and experimental projects, the range of possible activities and responsibilities for a Google Data Scientist is enormous and depends on the team and project. Depending on their size, scope, and importance, each project and product might have a dedicated engineering team as well as data scientists and managers.

Data scientists at Google are required to track and analyze key data points relevant to a project, and work with the engineering team to discover problems and propose solutions. Because they collaborate closely with engineers, Google Data Scientists are expected to have a strong working knowledge of the technical structure of each project they’re assigned to. Although they’re not expected to write code or build features as part of their responsibilities, being a performant DS requires a high level of technical skill.

Some key examples of what a data scientist might be assigned to are:

• Product development and improvement
• User health and safety
• Advertising and marketing optimization
• Cloud storage analytics
• Data security and accessibility

Most of these assignments can also be broken down into different products and teams, too, such as optimizing ads on YouTube, health and safety analysis on the Android OS, and so on.

If, for example, you were hired to work as a DS with a health and safety team, you would be tasked with tracking, analyzing, and acting upon any trends that could point to a potential violation of Google’s policies. You might analyze and determine whether a sudden growth of users in a particular city is organic or the result of malicious actors or bots. Being able to confidently track trends in data, slice datasets up effectively in order to identify trends, and draw informed conclusions about your findings are crucial.

The average compensation for Google Data Scientists is:

• L3: $161,000
• L4: $252,000
• L5: $378,000
• L6: $446,000
• L7: $611,000
• L8: $793,000

Before you apply

• Take the time to research as much as you can about the product and team you’re interviewing for. Learning about the size and scale of the product, the number of team members, the potential risks, and other key considerations will not only help you understand the role, but may also give you an idea of what the interviewers will ask you to work on in the final rounds.
• Google has a robust engineering blog that is broken down by vertical, with sections and posts dedicated to topics like AI, cloud hosting, web platforms, and so on. Reading posts in the section relevant to the role you’re applying for can give you an idea of what products and problems are top of mind. Remember that certain teams and roles may be separate from the Google blog, though—YouTube, for example, has its own engineering blog.
• Review your career and educational achievements and identify the projects and other areas that best express your leadership skills and excitement about data science. try
• Take some time to go through the most common DS questions asked at Google, expectations, and other considerations Google interviewers have when assessing a candidate.

Interview process

The DS interview process is similar for all levels, with higher technical expectations for L4 through L6 candidates. The process is:

1. A remote recruiter screening to assess your skills, experience, and culture fit
2. A remote tech screening to assess your general aptitude and knowledge of the domain you’re applying to work in
3. An onsite interview loop, with two interviews dedicated to testing your skills in depth and your ability to think through the specific considerations of the role. The final two rounds are a technical assessment and a culture and behavioral round.

1. Recruiter screening

The recruiter screening will be a remote video or phone conversation with a recruiter, who will ask you basic questions about your education, background, experience, and your desire to work at Google. A very important thing to keep in mind is that this call, like the rest of the process, will likely be team-specific. The hiring manager will have given the recruiter questions to assess you, so it’s critical that you’re prepared to answer domain-specific questions.

Some example questions:

• Why do you want to work on [Google team]?
• What are the biggest challenges when working in [domain] data?
• Talk about your experience working cross-functionally with engineers.

2. Technical screening

This remote interview with a hiring manager will be handled using a Google doc. Rather than a simple pass-fail, it’s an assessment of your basic technical skills and domain knowledge. You’ll be given a specific technical problem to solve on the doc, and then a more open-ended domain question—possibly one which the team is presently working on. Prior research into the team’s work and priorities can help you a great deal here, as the domain-focused question will drill down into your familiarity with the specific challenges the team is facing.

Some example questions:

• Reverse a linked list.
• Merge k sorted linked lists.
• Return all possible permutations of an array.

3. Onsite interviews

GCA and RRK interviews

This onsite round will begin with two interviews that concentrate on some key characteristics that Google uses to measure job applicants:

1. GCA: General Cognitive Ability
2. RKK: Role-Related Knowledge

GCA assesses a candidate’s general intelligence, skills, and ability to solve a problem—and to talk through their explanation as they solve it.

RKK is the team and domain-specific knowledge the candidate has, either through hands-on experience working in the space, or from research prior to the interview. For example, a data scientist working on cybersecurity would display familiarity and skills sufficient to work on projects like automated penetration testing, fraud detection, and data accessibility in a zero-trust system.

Because Google generally allows for team and role-specific interviews, your first two rounds will likely be focused on common or current case studies, which gives you the opportunity to display your skills and thought process about a problem your team is facing. For most data scientist roles, this will involve a conversational walkthrough with the interviewer, followed by work on a specific dataset in order to draw conclusions.

For example, if you were applying to a health and safety role, you’d be tasked with catching abuse and other malicious activity on one of Google’s platforms. An example case study would center on an alarming trend in the data you track, such as a massive uptick in comments or downvotes on YouTube videos. A common mistake applicants make at this step is to treat the case study as a call to action, and to immediately get to work “solving” the problem before they understand it. This approach is likely to result in a downlevel, as senior data scientists are expected to think through the different possibilities before “raising the alarm” or working directly with the data.

Instead, approach these rounds conversationally at first, asking the hiring manager questions and getting as much context as possible. For example, you could explore other, non-malicious reasons why YouTube comments might increase—such as a certain YouTube channel or vertical being promoted elsewhere, or the introduction of a new feature by another team at Google. Neither of these possibilities are necessarily alarming or malicious, nor do they indicate abuse, which is something you might miss if you rush into working directly with the dataset.

In this example, your RKK will come before the GCA, as your understanding of the details and factors that might influence an aberrant uptick in user activity will inform your approach. You’ll be expected to discuss your thinking and ask questions well before getting a dataset to work with. Your GCA will become a point of emphasis as you extract relevant portions of the data using a coding language of your choice in order to draw conclusions and recommend an action, if any.

You can score bonus points if you show an ability to think outside of your team and role, and respond in a cross-functional way that shows your understanding of the whole system. For example, you might theorize that a recently added YouTube advertising feature is incentivizing engagement, which would require you to collaborate with the advertising team on a solution.

Some example tasks you might be asked to complete:

• Your system detects a huge uptick in new accounts in a particular region. How do you approach this potential issue?
• How would you design an automated system to catch abusive comments on Google’s platforms?
• Google Cloud API calls have doubled over the past month. How do you determine the reason and recommend an action, if any?

Technical assessment

As with the prior rounds, this assessment will be a technical task, but will still focus on the specific problems and use cases relevant to the team you are applying to work with. You’ll need to demonstrate a high level of skill, especially for senior data scientist roles, and these assessments will test your ability to work through complex data slicing and analysis skills.

For example, for the YouTube health and safety team, you might be tasked with joining tables that track user actions and combine them with other characteristics, such as when they created their account, in order to determine whether they’re bots or organic YouTube users.

This assessment could involve other SQL skills, such as using window functions to parse datasets or using subqueries to check on your conclusions. You might find that a high number of new accounts were created in response to a new advertising tool, but only some of them are bots. Using subqueries to first analyze the differences between bot accounts and real ones, your SQL knowledge will help you create a profile of potentially malicious behaviors, which will allow you to determine further steps, such as an automated comment flagging feature.

As a follow-up, you could be asked to design a machine learning tool that can make these determinations and take action without direct input, or come up with a statistical framework to scale up these assessments to thousands or millions of users. The interviewer will want to hear about your process, so you’ll need to tease out the challenges and limitations to show your domain expertise.

Some example questions:

• Design a machine learning/LLM tool to help you track advertising performance.
• Determine whether user growth is organic or bots using a particular dataset.
• What is the best way to connect SQL databases and why?
• Data shows a massive decrease in user engagement. Find out why using these datasets and recommend a solution based on your findings.

Behavioral interview

In addition to GCA and RKK, Google uses two other traits to determine whether a hire is a good fit: leadership and Googliness, which are primarily assessed in the culture and behavioral interview. Trusting in these characteristics is a major component of Google’s growth and success, as it has allowed them to gather a large number of contributing employees with an enormous range of skill sets.

Leadership

Leadership at Google isn’t just about having an impressive job title or taking point during a meeting. They value employees who can handle the tougher parts of leadership, particularly situations when there was no clear “right” answer, you had to weigh tradeoffs, or you defended your assessment to a peer or manager. For a data scientist, this means being able to analyze data and justify your conclusions to others, speak honestly about any issues you see, and work with others on a solution.

Being able to speak to your leadership effectively will require you to take a close look at your past roles and accomplishments. Find moments of genuine conflict, whether interpersonal or conflict between organizational priorities, and explain why you took the actions you did. You should also explain what you learned and how you can do better next time.

Googliness

When companies empower teams to create their own interview questions and take control of the process, this speaks to the way they approach company culture. The autonomy Google grants is part of Googliness, as is the idea that Google has a highly-defined set of personal characteristics that all employees must embody. Although they have culture documents and values, they also acknowledge that each team has some discretion about what works best for them. Here are the key components of Googliness you should speak to:

• Ownership and autonomy
• Focus on the user
• Identify one thing to do really well
• Be comfortable with ambiguity and making tradeoffs
• Work quickly and collaboratively

One of the key reasons for Google’s success is their focus on users, which speaks to their heavy emphasis on data and analysis when building products and services. As you discuss your approach to problems with the interviewer, emphasize a focus on what user data tells you, rather than drawing a conclusion and trying to find data that justifies it.

Some questions you might be asked in the behavioral round are:

• Tell me about a time you put someone else’s interests above your own.
• Tell me about a time you brought disagreeing teams together.
• Tell me about a time when you launched a new initiative and how you went about it.
• Tell me about a time when you made a decision based on data and were wrong.
• Tell me about a time you had a conflict with someone. How did you resolve it and what did you learn?

Additional resources

• Google’s blog
• Google’s Research blog
• Google’s Developer blog
• Google’s Ten Things We Know To Be True philosophy document
• Google company commitments
• Google interview questions
• What's It Take to Work at Google?
• Data Scientist interview questions

FAQs about the Google Data Scientist interview

How should I prepare for a Google Data Scientist interview?

You should start by reviewing questions about data science and about Google’s behavioral rounds. Do as much research as possible about the team and role you are applying for, focusing on their projects and thinking about how they intersect with data science. You can also review Google’s philosophy and commitments to get a better understanding of their culture.

How much does a Google Data Scientist make?

According to Levels, Google Data Scientists are paid the following:

• L3: $161,000
• L4: $252,000
• L5: $378,000
• L6: $446,000
• L7: $611,000
• L8: $793,000

How long is the Google Data Scientist interview process?

According to users on Glassdoor, the Google Data Scientist interview process can take between 4–6 weeks, starting from the screening call and ending with an offer.

Does Google offer data science internships?

Google offers internships to undergraduate and graduate students, including research internships covering a wide range of topics. The internships usually last 12–14 weeks, with starting and ending times dependent on the location, as they are offered globally. You can learn more at their internship FAQ.

Do I have to have a degree to work as a data scientist at Google?

Depending on the job, you will be expected to have at least a BS in a relevant topic, such as statistics or computer science. Most roles will favor advanced degrees, although this isn’t a requirement.

Do I have to wear one of those hats?

The Noogler hats are still given to new Google employees, though it isn’t required that you wear one. We currently don’t offer fashion guides.