How to Get Hired as a Data Scientist at Google

How to Get Hired as a Data Scientist at Google

Everybody wants to work at Google, but how do you become one that works there?

Why would you even want to work at Google? Every person has different career goals, motivations, and reasons for choosing a certain career and an employer. However, I think it would be safe to reduce the multitude of reasons to two:

  1. Competitive salary
  2. Using and furthering your skills as a data scientist

These two reasons work together. Of course, you want to be well paid, especially when data science is a specific field requiring multidisciplinary knowledge and education. You want to get compensated fairly for the time, effort, and money you invested in your education.

While you need to make a living (unless you inherited a significant amount of wealth), why not make it a comfortable living while also doing something that you find interesting? You just don’t accidentally start working in data science, so it’s a safe bet that you’re here because you find data and data science interesting, regardless of money. And if you do, then you’d want to work at a top company that is a leader in innovation and the latest technologies. Working at such a company challenges your skills. It gives you the platform for developing them further than you’d be able in most other companies by participating in the most varied and technically-advanced data science projects.

Google Data Scientist Salary

One of the ways how Google attracts top data scientists is by offering them a competitive salary. As a data scientist at Google, you can earn well above (almost 250%) the US median salary, even in the most junior positions. It usually includes not only the basic salary but also the cash and stock bonuses.

Apart from this, other material benefits cover

  • Insurance, Health, and Wellness
  • Financial and Retirement
  • Home
  • Transportation
  • Perks and Discounts,

and other benefits unique to Google.

Some of these benefits are health & life insurance, 401k, Student Loan Repayment Plan, remote work, adoption and surrogacy assistance, transportation allowance, free lunch and drinks,  tuition reimbursement, etc. You can learn more about salaries, benefits, and levels in the Google Data Scientist Salary article.

Now, the Hard Part: Getting Hired!

Knowing what salary you can get is not essential for getting a job at Google. Moreover, it’s completely irrelevant, but it can serve as a good motivation for getting the hard part done. How do you do that? The approach is defined by three aspects you need to pay attention to:

  1. Knowing Google’s hiring process
  2. Having skills they need
  3. Acing job interview

1. What is Google’s Hiring Process?

What is Google Hiring Process

Knowing how Google hires is the first step in getting hired. From a high-level perspective, Google’s process is the same as in any other company and consists of:

  • Applying for a job
  • Interviews

However, details matter, and you should go into detail on what Google wants to see in your job application and what their interviews look like.

Job Application

Google strongly advises that you do a little self-reflection before you jump to applying for a job. This means they want you to think about your skills, interests, goals, and motivations. Even before applying, you can check if you’re the right fit for a job that way.  When thinking about your professional life and yourself as a person, think about whether you enjoy more working alone or as part of a team, what kind of job (or its part) you find most rewarding, what your passions are, do you get excited about solving a problem or discussing it, and so on.

Once you decide to apply for a job at Google, you should know the following:

  • Cover letters are not required
  • Tailor CV to the specific position (even if you apply for multiple positions) – generic CVs are a big no-no!
  • Keep CV concise and focused
  • Highlight the skills that are required for the job you apply for
  • Quantify the success at your previous job  – ‘successful’, ‘quicker’, ‘more efficient’, ‘disruption’, and ‘data-driven’ is not a metric
  • Mention your references – if you have somebody that can verify your work experience, projects you did, your character, and skills in general, that will ensure your resume will be looked at; it doesn’t guarantee you’ll get an interview, but it can increase your chances.

Interviews at Google

Before you come to the interview stage, ensure you know Google as a company well. Be informed about their history, organization, values, and products. This will show you’re really interested in working at Google and that you didn’t apply for a job accidentally. Imagine that you send your resume, you come to the interview, and the interviewer doesn’t know your name or anything about your education or work history. You wouldn’t be happy, would you? The same goes for Google: they like to see that what you know about them makes you want to work for them.

The first step before the interviews with Google is a phone call with the recruiter. They will ask you a few general questions about your work experience and interest in working at Google. They might also ask a simple technical question or two (e.g., a probability question or something easily solved in a minute) to get a general idea of whether you’re suitable for the position. However, the main point of this call is to understand your work experience and how it aligns with what Google is looking for.

Then comes the interview process at Google, and there’s no big mystery here: Google itself lists the types of interviews you could expect. You just need to take some time to inform yourself about it and be prepared.

Online assessment is the first elimination stage when it comes to interviews. This is usually a short test of your coding skills conducted online.

If you get past this, then comes short virtual chat or two. These are not on-premises but over the phone or video chat. They will involve a recruiter, hiring manager, and/or a colleague from the team asking you about the skills required for the job you applied for. The point is for them to get a picture of your technical profile and if it generally suits the position. They can also learn that even though you may be missing some of the required skills, you have some other skills that can be well used in a particular job.

Depending on the job, Google might ask you to do project work. This means doing a little project or providing some of your previous work/code.

All these steps are where the candidates get eliminated before they get to the in-depth interviews. There are usually 3-4 interviews in one day, intended to assess your technical skills, problem-solving and thinking process, and personality traits.

When it comes to testing your expertise, this is usually done through the following types of questions. They don’t come up every time; the types of questions you get heavily depend on the position you applied for.

  • Coding Questions
  • Algorithm Questions
  • Statistics Questions
  • Modeling Questions
  • Business Case Questions
  • Product Questions
  • Technical Questions

You can find more about the whole process on the Google website.

While you’re making yourself familiar with Google’s hiring process, use this.

2. What Skills Google Wants to See in Data Scientists?

What Skills Google Wants to See in Data Scientists

There’s no such thing as an ideal candidate. Google knows that because Google knows everything. Every candidate has unique skills and characteristics that could make them a desirable candidate. Google tries to select the candidates with the best combination of:

  • Hard skills and qualifications,  and
  • Soft skills

Hard Skills and Qualifications

The general requirements for data scientists at Google are:

  • Master's Degree in Statistics, Computer Science, or other relevant quantitative disciplines
  • Relevant experience, which you’ll need more of to compensate for if you’re lacking a required formal education level
  • Programming languages: SQL and R/Python

Depending on the position you’re applying for, some other specific requirements can be focused on the following areas: statistics, machine learning, AI, data analysis, data visualization, engineering, software development, products, etc.

Soft Skills

Getting hired at Google requires high scores at:

  • Interdisciplinarity
  • Big-picture Perspective
  • Being Customer-Oriented

Data science is an interdisciplinary field per se. It merges statistics, mathematics, and business knowledge. This interdisciplinarity is compounded by the requirement for data scientists to work with other sectors in Google, such as Product, Marketing, Engineering, etc.

The essence of data science is problem-solving with the business outcome in mind. To solve problems, every company (including Google) introduces projects. The only way to complete the projects successfully is to be focused on the project outcome and know how to achieve this goal. With such a desirable skill, it’s no wonder Google wants to see big-picture energy from their data scientists.

Every data science project at Google has business in mind, and when we say business, it means customers. All you do will, directly or indirectly, be used by Google’s customers. Their satisfaction is the key for Google keeping their market-leader position, as is for you getting the job. Show that you have this in you, and you’re one step closer to becoming a data scientist at Google.

The final step for achieving this is performing well in the interviews.

To get more details about Google’s hiring process and the skills they’re looking for, take a look at The Ultimate Guide to Become a Data Scientist at Google.

3. Acing Job Interview

Acing Google Job Interview

The central part of all the interviews you’ll have at Google are, one way or another, your technical skills.

While you for sure don’t know which questions you’ll get, there are still ways for you to better your chances of getting a data science job.

  • Brush up your skills
  • Have a clear approach to coding questions
  • Be self-aware

Brushing Up Your Skills

You’re preparing for a job interview, right? Solving the actual job interview questions before the real job interview at Google seems quite logical.

There are platforms where you can do that—for example, StrataScratch, LeetCode, SQLPad, or HackerRank. There you can practice your SQL, Python, algorithm and other technical skills tested by Google.

Also, there are other ways to refresh your knowledge or learn something new. You have course websites (e.g., Coursera, Udemy, edX), Youtube channels (e.g,, Alex the Analyst, Amigoscode), blogs (, GeeksforGeeks, W3Schools), and data science community (Stack Overflow, Reddit, GitHub, Codementor) at your disposal. While they don’t necessarily prepare you specifically for a Google job, these resources (and many others) can help you with the data science concepts you can easily apply at the Google job interview.

Framework for the Coding Questions

It is crucial to write a correct solution when you’re at the coding interview, I don’t deny that. The pressure and limited time at the interviews can make even the most experienced look a level or two below their natural coding-masters selves.

To get around this, I advise that you always have a clearly defined framework of how to approach solving the coding questions. I found that these four general guidelines work best:

  • Explore the dataset
  • Identify relevant columns
  • Write out the code logic
  • Code

Exploring the dataset

Exploring the dataset involves getting to know each table’s data structure. It also means detecting the shared columns between the tables, thus knowing how the tables can communicate. Along the way, get a sense of data types in each column and whether there might be duplicate or NULL values.

Identify relevant columns

When you identify the relevant columns, you will eliminate the unnecessary columns that can clutter your thinking and divert you when writing a code. The interview questions often give you more data than you need, reflecting a data scientist's real life. Consider this as a small test where you can show that you can differentiate between relevant and irrelevant data.

Write out the code logic

Before you start coding, it’s important to write out all the steps of your solution. Break down the code into logical blocks and/or individual steps, and decide on the functions you will use, why you will use them, and how. The code logic can be written in English (or any other language the interview is conducted in) or a pseudo-SQL/R/Python/any other programming language code.


Coding should, at this point, feel almost like a technicality. All the previous steps will make it possible for you to focus on the code syntax, its efficiency, and debugging. It also allows you to check the code logic and catch all the missing or unnecessary steps.

This is how this framework can be applied to the Google Data Scientist Interview Questions.


Job interviews are stressful, draining, and require a lot of concentration. They are, to be honest, a pain in the ass. They can sometimes show the worst side of our characters. Don’t let this happen to you by considering three simple things.

  • Slow down
  • Be friendly
  • Listen to the interviewers

When under stress, people tend to jump to answers, stop taking time for thinking, and talk too fast. Slow down.  Allow yourself time to think about what you’re going to say, ask for clarifications if you didn’t understand what was being asked, and try to be as articulate when you talk.

People often wrongly think that silence between the interviewer’s question and your answer shows you’re a slow thinker, low on self-confidence, or whatnot. No, it shows that you’re thinking and not simulating it. It shows you’re confident enough to take your time to come up with the best possible answer, which ultimately shows you can control stressful situations. Highly desirable skills for a data scientist!Also, if you talk at a medium pace, the chance is better that you won’t blurt out something stupid. And everything smart that you say will be easily followed and acknowledged as smart by the interviewer.

Be friendly. You won’t be working alone in a cave atop some hill. Want it or not, you’ll work within a team and cooperate with other teams. Having different personalities in a team or across the teams is desirable. But this has limits. Nobody wants to work with a person that drains all the energy from other people, starts petty fights, takes credit for someone else work or sabotages everybody else. Google wants people who others enjoy working with, so remaining friendly and good-spirited under pressure is something they’ll look for. The interview is a perfect opportunity to showcase this side of yourself.

Listening is equally important as talking. Pay attention to what the interviewer asks so you can answer their questions. Don’t interrupt them, but ask questions if you want something to be clarified.


Getting hired at Google starts with knowing what you want and finding an ad for a job that you’d like. Then comes the part where you apply for a job by satisfying specific format requirements and make yourself familiar with Google itself: its hiring process and other aspects of the way they operate.

When you come to the interview stage, you must know what you can expect there: what types of interviews they conduct and the topics. Once you know that, you should prepare yourself as best as possible. Use various sources, such as job interview questions examples, Youtube channels, blog articles, and courses, or get involved with the data science community to ask about technical concepts and their experiences on getting hired by Google.

These steps prepare you to shine in a job interview, where you can confidently showcase your hard and soft skills. In other words, the best version of yourself.

How to Get Hired as a Data Scientist at Google

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