Data scientist interviews at Google are challenging. The questions are difficult, specific to Google, and cover a wide range of topics.

The good news is that the right preparation can help you maximize your chances of landing a job offer at Google (or Google Cloud). We’ve analyzed 90+ data scientist interview questions reported by real Google candidates to determine which topics come up most frequently.

Below you’ll find our ultimate guide for success, including example questions, links to solutions, and a preparation plan to help you land that Google data scientist role.

Here's an overview of what we'll cover:

##### Click here to practice 1-on-1 with a data science ex-interviewer

## 1. Google Data Scientist Role and Salary**↑**

Before we cover your Google data scientist interviews, let’s first look at the role itself.

### 1.1 What does a Google Data Scientist do?

Data scientists at Google (formerly known as quantitative analysts) are responsible for processing, analyzing, and interpreting data sets and using them to evaluate and make recommendations to improve Google’s various products. They’re vital to the company’s optimization and decision-making process.

As a data scientist, you will be working closely with diverse teams of engineers, product managers, sales and marketing teams, and analysts. You will be using your expertise in data analytics and statistical methods to study and derive insight from user behavior, design, conduct, and measure product improvement experiments, etc. Google data scientists are also in charge of developing scalable models through the use of machine learning algorithms.

Because Google is a data-driven company, data scientists apply their data expertise to work on various product lines and business functions. These include Google Search, Ads Insights and Measurement, Google One, Google Play, Google’s compute infrastructure, Customer Engineering support, Google’s applied machine learning, Cloud Supply Chain Operations (CSCO), AI Safety Protection Team, Google Technical Services, and many more.

**What skills are required to be a Google Data Scientist?**

An analysis of current data scientist posts at Google shows that the minimum requirement for a Google data scientist is a Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field, plus 5 to 10 years of work experience with bit data, analysis applications, and coding. However, having a Master’s degree or PhD in the abovementioned fields is preferred. Some posts will require a Master’s degree as a minimum requirement. You’ll also need to have a mastery of at least one statistical language (R or Python) and one database language (SQL), as well as demonstrated knowledge of machine learning algorithms.

Aside from technical expertise, Google will be looking out for your communication skills. As a data scientist, you’ll be working with cross-functional teams, some of whom may not be technical. You may also be in charge of visualizing data to communicate your findings with different levels of stakeholders.

### 1.2 How much does a Google Data Scientist make?

Based on the computations from Glassdoor data, the average data scientist salary at Google is 39% higher than the estimated average salary of a data scientist in the US.

Location also plays a part in the difference in salary based on Glassdoor data. To compare:

- Google India Data Scientist: est. average of $18k/year base pay
- Google US Data Scientist: est. average of $168k/year base pay

Below you can see the average salary and compensation of the different data scientist levels at Google US, as of early 2024, based on Levels.fyi.

Ultimately, how you do in your interviews will help determine what you’ll be offered. That’s why hiring one of our ex-Google interview coaches can provide such a significant return on investment.

And remember, compensation packages are always negotiable, even at Google. So, if you do get an offer, don’t be afraid to ask for more. If you need help negotiating, consider booking one of our salary negotiation coaches to get expert advice.

2**. Google Data Scientist Interview Process and Timeline↑**

**2.1 What interviews to expect**

What's the Google data scientist interview process and timeline?

It typically takes three to six weeks and follows the steps below. If you're interviewing at Google Cloud Platform, you can expect similar steps.

- Resume screen
- Recruiter screen (~30 min)
- Technical screen (~45-60 min)
- Onsite interviews (5 interviews, 45 min each)

Note that the exact process varies slightly between positions, as Google data scientists may be working in research, product analysis, or in other areas. Your recruiter will send you information at the beginning of the process, which will detail what interviews you can expect.

Google has a dedicated Candidate Accommodations team that will help you through your application process if you require any special assistance at any point in the process. Be sure to fill out the accommodation form so that you can get the assistance you need.

Let's look at each of these steps in more detail below:

#### 2.1.1 Resume screen

Step one: apply. As this guide focuses primarily on practice questions, the application overview section will be brief. Note that the Google data scientist role used to be titled “quantitative analyst,” and some job postings may still retain that term.

First, recruiters will look at your resume and assess if your experience matches the open position. This is the most competitive step in the process, as millions of candidates do not make it past this stage. Check out our free Google resume guide with examples for help on writing yours.

If you’re looking for expert feedback on your resume, you can get input from our team of ex-Google recruiters, who will cover what achievements to focus on (or ignore), how to fine-tune your bullet points, and more.

After applying, you will receive an email containing a pre-screen questionnaire of introductory technical questions before moving on to the full technical screen. If you are prepared for the full technical screen, this preliminary questionnaire will not be difficult. Applicants with referrals or who have been contacted directly by recruiters via LinkedIn may be able to skip the pre-screen online assessment.

#### 2.1.2 Recruiter phone screen

After this, many but not all applicants talk to an HR recruiter on the phone. This call is an opportunity to learn more about the interview process ahead of you as well as how to prepare. Come ready to answer questions about your professional background and why you’re interested in Google. As this is followed by a technical screen, candidates are rarely asked coding or statistical questions at this step.

However, some candidates report passing directly to the technical screen after the initial application. In this case, the technical screen will include a few background questions that would otherwise have been asked in the recruiter call. Be sure to specify with your recruiter ahead of time what type of call you’ll be receiving so that you can come prepared.

#### 2.1.3 Technical screen

After the application and pre-screen questionnaire, you’ll move on to a video call with a hiring manager, recruiter, or one of Google’s data scientists. This will take place over Google Hangouts. In very rare cases, you may undergo two technical screens before the onsite round. If you’re not sure what to expect, check in with your recruiter.

We’ll go into greater detail on the questions themselves later in this article, but in general be prepared for a few background questions, followed by statistical questions and coding. You’ll be coding live in a language of your choice on a shared document. Clear communication is important to Google, so practice talking through your reasoning simply and coherently as you work.

#### 2.1.4 Onsite interviews

The final and toughest stage of the Google data scientist interview process is the onsite portion. Typically this involves five rounds of interviews that last about 45 minutes each, with time for lunch in the Google cafeteria. Other than lunch, you may have little to no breaks between interviews.

You will need to be prepared for many types of questions during the onsite interviews, with an emphasis on applied statistics in business case scenarios. Prepare for a higher level of difficulty than the questions presented during the technical screen.

More specifically, there are four main categories of questions that you’ll have to answer. We’ll give you practice examples later, but here’s a summary:

**Statistics and machine learning questions**, where you’ll be tested both on general statistical principles and definitions as well as probability and applied machine learning.**Coding questions:**where you’ll demonstrate both technical skills and statistical problem solving via SQL querying and programming in the language of your choice.**Behavioral questions**, where Google will test your culture fit through your past experiences and current motivations.**Product sense questions, where you’ll need to apply your statistical and coding skills to test and drive business and product decisions.**

After you’ve completed the onsite rounds of interviews, you should receive feedback in a matter of weeks.

**2.2 What exactly is Google looking for?**

At the end of each session, your interviewer will grade your performance using a standardized feedback form that summarizes the attributes Google looks for in a candidate. That form is constantly evolving, but we have listed the main components we know of at the time of writing this article below.

#### A) Questions asked

In the first section of the form, the interviewer fills in the questions they asked you. These questions are then shared with your future interviewers so you don't get asked the same questions twice and to ensure a well-rounded interview.

**B) Attribute scoring**

In the next section, each interviewer will assess you on the four main attributes Google looks for when hiring:

**General cognitive ability.**This is often referred to as "GCA" by Googlers. The company wants to hire smart data scientists who can learn and adapt to new situations. Here your interviewer will try to understand how you solve hard problems and how you learn. For more information, take a look at our guide to the Google GCA interview.**Role-related knowledge and experience.**This is often referred to as "RRK" or "RRKE" internally. The company wants to make sure that you have the right experience, domain expertise and competencies for the position you're applying for. For more information, take a look at our guide to the Google RRK interview.**Leadership.**Google looks for a particular type of leadership called “emergent leadership.” You'll typically be working in cross-functional teams at Google, and different team members are expected to step up and lead at different times in the lifecycle of a project when their skills are needed.**Googleyness (i.e. culture fit).**The company wants to make sure Google is the right environment for you. Your interviewer will check whether you naturally exhibit the company's values, including comfort with ambiguity, a bias to action, and a collaborative nature.

Depending on the exact job you're applying for, these attributes might be broken down further, but the total number of attributes does not usually exceed six or seven.

In this middle section, Google's interviewers typically repeat the questions they asked you, document your answers in detail, and give you a score for each attribute (e.g. "Poor", "Mixed", "Good", "Excellent").

**C) Final recommendation**

Finally, interviewers will write a summary of your performance and provide an overall recommendation on whether they think Google should be hiring you or not (e.g. "Strong no hire", "No hire", "Leaning no hire", "Leaning hire", "Hire", "Strong hire").

**2.3 What happens behind the scenes**

If things go well at your onsite interviews here is what the final steps of the process look like:

- Interviewers submit feedback
- Hiring committee recommendation
- Team matching
- Senior leader and Compensation committee review
- Final executive review (only for senior roles)
- You get an offer

After your onsite, your interviewers will all submit their feedback usually within two to three days. This feedback will then be reviewed by a hiring committee, along with your resume, internal referrals, and any past work you have submitted. At this stage, the hiring committee will make a recommendation on whether Google should hire you or not.

If the hiring committee rules in your favor, you'll usually start your team matching process. In other words, you'll talk to hiring managers and one or several of them will need to be willing to add you to their team in order for you to get an offer from the company.

In parallel, the hiring committee recommendation will be reviewed and validated by a senior manager and a compensation committee who will then decide how much money you are offered. Finally, if you are interviewing for a senior role, a senior Google executive will review a summary of your candidacy and compensation before the offer is sent to you.

As you've probably gathered by now, Google goes to great lengths to avoid hiring the wrong candidates. This hiring process with multiple levels of validations helps them scale their teams while maintaining a high caliber of employees. But it also means that the typical process can spread over many weeks and sometimes months.

## 3**. Google Data Scientist Example Questions↑**

Let’s get into the four primary categories of questions you’ll answer during the Google data science interview:

- Statistics and Machine Learning (56% of reported questions)
- Coding (26%)
- Behavioral (9%)
- Product Sense (9%)

Note that many of these questions are asked in the form of case studies. Take a look at our data science case study interview guide for more information.

In the sections below, we've put together a high-level overview of each type of question. In addition, we've compiled a selection of real Google data scientist interview questions, according to data from Glassdoor. We've edited the language in some places to improve the clarity or grammar, and we've included a link to a solution when viable.

### 3**.1 Statistics and machine learning questions (56%)**

Google’s data scientists have to derive useful insights from large, and potentially complex, datasets. Thus it’s imperative to have a strong understanding of statistics. Out of all the question categories, general statistics and statistical probability come up the most often in all stages of the interview process. Take extra time to study this section.

Review fundamental statistics and how to give concise explanations of statistical terms, with an emphasis on probability. Some general topics that have come up before include p-values, MLE, confidence intervals, and Bayes theorem. In addition to these general topics, you’ll find complete questions to work through below.

Your interviewer will also ask questions specific to machine learning, as Google data scientists must build algorithms that improve and remain accurate over time. General topics that have come up before include regression models, feature selection, and recurrent neural networks.

Let's get to the example questions.

**Google data scientist interview questions - Statistics and machine learning**

**General Statistics**

- In what situation would you consider mean over median?
- For sample size n, the margin of error is 3. How many more samples do we need to make the margin of error 0.3?
- What is the assumption of error in linear regression? (Solution)
- Given data from two product campaigns, how could you do an A/B test if we see a 3% increase for one product?
- Is it good or bad if you apply bootstrapping on samples to increase your sample size?
- Predict some metrics using regression.

**Statistical Probability**

- I have a deck and take one card at random. What is the probability you guess it right?
- Explain a probability distribution that is not normal and how to apply that.
- Given uniform distributions X and Y and the mean 0 and standard deviation 1 for both, what’s the probability of 2X > Y? (Solution)
- There are four people in an elevator and four floors in a building. What’s the probability that each person gets off on a different floor?
- Make an unfair coin fair. (Solution)

**Machine Learning**

- If the labels are known in a clustering project, how would you evaluate the performance of the model?
- Why use feature selection? (Solution)
- If two predictors are highly correlated, what is the effect on the coefficients in the logistic regression? What are the confidence intervals of the coefficients?
- What is the difference between K-mean and EM?
- When using a Gaussian mixture model, how do you know it is applicable?
- Derive the maximum likelihood estimator for logistic regression.
- What happens to regression coefficients if you have omitted variable bias?
- Explain the difference between supervised learning and unsupervised learning. Give examples of each.
- Describe to me how PCA works.
- How would you evaluate the performance of a machine learning algorithm?

### 3**.2 Coding questions (26%)**

Google data scientists work with the company's vast datasets to understand and solve real-world problems. So expect Google interviewers to test you on statistical coding, SQL, and some data analysis.

As statistical questions come up the most often, be ready to write functions that solve problems related to statistical analysis and probability. Most candidates report coding with Python, but you may use your preferred programming language.

SQL questions are the second most frequent technical topic, followed by a mixture of data structures and modeling. Google is looking for candidates who know how to use and manipulate important data. So practice running SQL queries quickly and with proper syntax.

Finally, we recommend reading this guide on how to answer coding interview questions and practicing with this list of coding interview examples in addition to those listed below.

**Google data scientist interview questions - Coding**

**Statistical coding questions **

- Write a function to generate N sample from a normal distribution and plot the histogram. (Solution)
- Write code to generate iid draws from distribution X when we only have access to a random number generator.
- Coding in R, multiply all a[i,j] in a i rows j columns dataset.
- Given a list of characters, a list of prior probabilities for each character, and a matrix of probabilities for each character combination, return the optimal sequence for the highest probability.
- Given a list of numbers, calculate the sum of the odd numbers in the sequence of 100 numbers (I.e. sum(1st, 3rd, 5th … Nth number for in sequence ).)

**SQL**

- How would you find the top 5 highest-selling items from a list of order histories?
- Can you explain how SQL works?
- Given three columns of data, how would you compare the first three to the last three?
- How do you calculate the median for a given column of numbers in a data set?
- How would you optimize a database query?

**Other: e.g. data structures, modeling**

### 3**.3 Behavioral questions (9%)**

In addition to the question types highlighted above, you can expect to be asked behavioral or "resume" questions about your past work experience and your motivation for applying to Google. Your interviewers are looking for you to demonstrate your “Googleyness” as well as your ability to communicate clearly.

If you're applying directly to a job posting, be strategic by aligning your answers for behavioral questions with the top qualifications that are listed in the job description. Below you’ll find the real behavioral interview questions reported by data scientist candidates.

Practice using the example questions below.

**Google data scientist interview questions - Behavioral**

- Why Google?
- How do you sort your priorities when engaged in multitasking?
- Describe a past project you worked on.
- In what direction do you see your career moving?
- Do you prefer working in small or large teams?
- How do you push back when disagreeing with a manager?
- What are the top competencies that you are bringing to our company?
- Why do you prefer Google over Apple?
- How is this job aligned with your objectives?

### 3**.4 Product sense questions (9%)**

Google's data scientists must be able to use their technical skills in order to drive concrete business decisions. Through a variety of techniques, data scientists generate insights that are ultimately used to test and improve Google's products as well as the company as a whole.

So come prepared to apply your technical knowledge to business case scenarios. For example, Google tends to ask questions that use statistical A/B testing to compare the performance of their products and services. You should also be prepared for questions about product metrics and how they could be improved. Don’t forget to familiarize yourself with Google’s main products in advance.

Practice using the example questions below.

**Google data scientist interview questions - Product sense **

- You have a Google app and you make a change. How do you test if a metric has increased or not? (Solution)
- How do you detect viruses or inappropriate content on YouTube?
- How would you compare if upgrading the android system produces more searches?
- The outcome of an experiment is that 5% of one group clicks more. Is that a good result?
- How would you measure the time spent in Google Search per day per user? If the average searches per day per user data goes down, but the average searches per country goes up, how would you explain it?
- How would you remove bias and make inferences from data about two ad campaigns?
- Given there are no metrics being tracked for Google Docs, a product manager comes to you and asks, what are the top five metrics you would implement?
- If product A had a feature and the team wanted to change it, how would you use data science to give recommendations to the team?
- How would you study the relationship between hours of YouTube watched vs age? What about confounds? Zip code, etc.

## 4. Google Data Scientist Interviewing Tips**↑**

You might be a fantastic data scientist, but unfortunately, that won’t necessarily be enough to ace your interviews at Google. Interviewing is a skill in itself, that you need to learn.

Let’s look at some key tips to make sure you approach your interviews in the right way.

### 4.1 Ask clarifying questions

Often the questions you’ll be asked will be quite ambiguous, so make sure you ask questions that can help you clarify and understand the problem. Most of the questions will focus on testing your technical proficiency.

### 4.2 Be conversational

Google wants to know if you have excellent communication skills. So make sure you approach the interview like a conversation.

Since Google will also be testing you on your ability to communicate highly technical concepts to non-technical people, be sure to brush up on your basics and practice interpreting them in a way that’s clear and easy for everyone to understand.

### 4.3 Think out loud

You need to walk your interviewer through your thought process before you actually start coding. Google recommends that you talk even while coding as they want to know how you think. Your interviewer may also give you hints about whether you’re on the right track or not.

### 4.4 State and check assumptions

You need to explicitly state assumptions, explain why you’re making them, and check with your interviewer to see if those assumptions are reasonable.

### 4.5 Present multiple possible solutions

Present multiple possible solutions if you can. Google wants to know your reasoning for choosing a certain solution. Google also wants to see how well you collaborate. So when solving problems, don’t hesitate to ask further questions and discuss your solutions with your interviewers.

Also, if you have a moonshot idea, go for it. Google likes candidates who think freely and dream big. So if the question allows it, try to find a way to display your creative and innovative thinking.

### 4.6 Be honest and authentic

Be genuine in your responses. Google interviewers appreciate authenticity and honesty. If you faced challenges or setbacks, discuss how you improved and learned from them. If you’re asked about your failures, don’t disguise them as strengths. Google values intellectual humility; admit where you went wrong and what you were able to learn from the failure.

### 4.7 Center on Googleyness

Familiarize yourself with Google’s core values and align your behavioral responses with them. Google values certain attributes such as comfort with ambiguity, collaborative nature, bias for action, and focus on the user.

### 4.8 Brute force, then iterate

When coding, don’t necessarily go for the perfect solution straight away. Google recommends that you first try and find a solution that works, then iterate to refine your answer.

### 4.9 Keep your code organized

Make sure to keep your code organized so your interviewer won’t have a hard time understanding what you’ve written. Google wants to see that your code has captured the right logical structure.

### 4.10 Get comfortable with coding on various mediums

Google now typically asks interviewees to code in a Google doc. But this can vary, it could be on a physical whiteboard or a virtual one. Check with your recruiter what it will be and practice it a lot.

## 5**. Preparation Plan↑**

Now that you know what questions to expect, let's focus on how to prepare. After all, the right preparation will make the difference between failing your Google interviews and getting an offer.

Below is our four-step prep plan for Google or GCP. If you're preparing for more companies than just Google, then check our generic data science interview preparation guide.

**5.1 Learn about Google's culture**

Most candidates fail to do this. But before investing tens of hours preparing for an interview at Google, you should take some time to make sure it's actually the right company for you.

Google is prestigious and it's therefore tempting to assume that you should apply, without considering things more carefully. However, it's important to remember that prestige alone won't make you happy in your day-to-day work. What will make you happy is what you’ll actually be doing as well as the people you'll be working with.

If you know data scientists who work at Google or used to work there, talk to them to understand what the culture is like. In addition, we would recommend reading the following resources:

- Google's mission statement (by Google)
- Google's values (by Google)
- Google strategy teardown (by CBS Insights)
- How to demonstrate Googleyness (by IGotAnOffer)

**5.2 Practice by yourself**

As mentioned above, you'll encounter four main types of interview questions at Google: statistics and machine learning, coding, product sense, and behavioral.

To get an idea of real-life problems that Google data scientists have to tackle on the job, take a look at The Unofficial Google Data Science Blog. For practice tackling case studies, take a look at our guide to data science case interviews.

**For statistics and machine learning interview questions**, we'd recommend brushing up on the fundamentals using Google’s own technical development guides. Brilliant.org offers online courses designed around statistical probability and other useful topics, some of which are free. Search for specific questions and answers around statistics, machine learning, data analysis, and others on StackExchange. Finally, you can post your own questions and discuss topics likely to come up in your interview on Reddit’s statistics and machine learning threads.

**For coding interview questions**, start with the video below which shows a step-by-step method for answering coding questions. It is aimed toward Amazon software development but may be useful for any type of coding.

Practice the method using example questions such as those in section 3, or those relative to similar Google positions (e.g. Google software engineer coding questions).

Also, practice SQL and programming questions with medium- and hard-level examples on leetcode, and explore Google's Tech Dev resource library for more questions. For extra help with SQL, read this analysis of the 3 "types" of SQL problems. Note that in the onsite rounds, you’ll likely have to code on a whiteboard without being able to execute it, so practice writing through problems on paper.

**For product sense interview questions**, you're dealing with problems that are similar to what product managers at Google would work on. As a result, we'd recommend studying our product management guides on metric, favorite product, product improvement, and estimation questions. These guides will equip you with a method for answering the majority of the product/business sense questions you're likely to encounter as a data scientist candidate. Again, study up on Google’s main products so you’re ready to dive into your reasoning about them.

**For behavioral interview questions**, we recommend learning our step-by-step method for answering behavioral questions. You can then use that method to practice answering the example questions provided in section 3.3 above.

Finally, a great way to practice all of these different types of questions is to interview yourself out loud. This may sound strange, but it will significantly improve the way you communicate your answers during an interview. Play the role of both the candidate and the interviewer, asking questions and answering them, just like two people would in an interview. Trust us, it works.

**5.3 Practice with peers**

Practicing by yourself will only take you so far. One of the main challenges of data scientist interviews at Google is communicating your different answers in a way that's easy to understand.

As a result, we strongly recommend practicing with a peer interviewing you. If possible, a great place to start is to practice with friends. This can be especially helpful if your friend has experience with data scientist interviews, or is at least familiar with the process.

**5.4 Practice with ex-interviewers**

Finally, you should also try to practice data science mock interviews with expert ex-interviewers, as they’ll be able to give you much more accurate feedback than friends and peers.

If you know a data scientist or someone who has experience running interviews at Google or another big tech company, then that's fantastic. But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can practice 1-on-1 with ex-interviewers from Google and other leading tech companies. Learn more and start scheduling sessions today.