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Google Data Scientist Interview Guide

Interview Guide Nov 25

Detailed, specific guidance on the Google Data Scientist interview process - with a breakdown of different stages and interview questions asked at each stage

The role of a Google Data Scientist

As a Data Scientist working at Google, you will be working on figuring out ways to improve Google’s products through data informed strategies.. You'll collaborate with a team of Engineers and Analysts on a wide range of problems, such as using statistical methods for the challenges of measuring quality, improving consumer products, and understanding the behavior of end-users, advertisers, publishers etc.

At Google, data drives pretty much all major decision-making. Using analytical skills and statistical methods, you’d be mining data to identify opportunities for Google and their clients to operate more efficiently.

Data Scientists at Google work across diverse domains, collaborating with teams throughout the company to tackle a wide range of challenges. They apply their expertise in data science, machine learning, and analytics to domains such as search and information retrieval, advertising and marketing, natural language processing (NLP), machine learning infrastructure, and user experience and product improvement. For example, Google has recently been hiring DSes for the advertising domain where the core responsibilities included - providing support in media strategy, measurement and optimization that require expertise in advanced analytics work, with special focus on Marketing Mix Modeling (MMMs).  This is just an example, of course. There are plenty of other roles - and correspondingly nuances of the craft you’d be expected to over-index on;; but we hope this gives you a flavor for what the role could look like.

How to Apply for a Data Scientist Job at Google?

Check out Google’s career page and browse through the Data Scientist job listings. When you find a role that interests you, be sure to read through the job requirements and qualifications carefully to ensure you meet the criteria. When you apply, make sure to tailor your resume to align with the qualifications listed in the job posting. This will help you stand out from other applicants. 

If you need help with customizing your resume specifically for Google (or for that matter, any other company), Prepfully provides resume review services - in this case we would recommend talking to one of our Google DS Recruiters or Google DSes to get very targeted advice on what you’ve covered well and where you need to improve/modify stuff. 

Google Data Scientist Interview Guide

As a part of the Google Data Scientist interview, you will need to go through multiple interview rounds. The interview process and questions may differ a bit across roles

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. Technical Screening - This round focuses on evaluating your technical skills in statistics, probability, and basic coding.

3. On-site Interview - The final round is the on-site round, where you will meet multiple Data Scientists and potentially a Hiring Manager. This round typically consists of several rounds designed to assess different aspects of your skills and qualifications. Note that these rounds depend on the domain you are applying in.

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Google Data Scientist: Recruiter Screening

Overview

The first round is a phone screening conducted by a recruiter. During this conversation, the recruiter will inquire about your data science background and relevant projects, aiming to gain a comprehensive understanding of your experience and skills. They will assess your skill sets based on the information provided in your resume and seek to gauge your proficiency in areas such as statistical analysis, machine learning, programming, and data manipulation. The screening will also evaluate your cultural fit within the company - or what Google typically refers to as “Googleyness”, ensuring that you align with Google's values and collaborative work environment - and will be a good fit for the company.

Interview Questions

  • Why do you want to join Google?
  • Why do you think you will be a good fit for the role?
  • How many years of experience do you have in data science?
  • What is your understanding of data science?
  • What are you passionate about?
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Google Data Scientist: Technical Screening

Overview

The second round is the technical screening. This round focuses on evaluating your technical skills in statistics, probability, and basic coding. For the coding questions, you can expect questions that require data manipulation, data intuition, and statistical analysis. Unlike applied situations or A/B testing, this round emphasizes math and stats-based questions to assess your foundational knowledge.

During the technical screening, the interviewers will also closely examine the projects listed on your resume. They will ask detailed questions to understand your role, contributions, and the technical aspects of these projects. It's essential to be well-prepared to discuss your resume projects, demonstrating your expertise and ability to apply data science concepts in real-world scenarios.

Some Prepfully candidates also reported being given a hypothetical business question in which you will be asked about which metrics you’d use to track the successful validation or invalidation of a hypothesis. Or potentially something more abstract; such as how you’d measure the success of a product. So be on the lookout for such questions.

Interview Questions

  • How would you forecast a brand's sales?
  • Is it good to apply bootstrapping on samples to increase your sample size?
  • Given 3 coin tosses of a fair coin results in Heads. What is the probability that the 4th coin toss will also result in a Head?
  • When do you use mean vs median?
  • Write a function that draws N samples from a population with mean = 0, SD = 1 and plot the histogram.
  • 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 ).)
  • Talk about a data science project you’ve worked on, and your contribution.
  • What’s the probability of a type 1 error? What happens when the sampling distribution is altered (e.g. filtering out all values below the mean) and how does it affect type 1 errors?
  • Suppose you are tasked with improving the user engagement of a mobile app. What metrics would you consider to measure engagement, and why?
  • Write a code to implement a hash table in Python.
  • Write a code to invert a binary tree in a programming language of your choice.

Not feeling confident about the Technical Screen? Book a mock with our Google Data Scientist Experts

→ Book a Mock interview here
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Google Data Scientist: On-site Interview

Overview

The final round is the on-site round, where you will meet multiple Data Scientists and potentially your Hiring Manager. This round typically consists of several rounds designed to assess different aspects of your skills and qualifications. Note that these rounds can sometimes change to specialize more on a specific domain you might be applying for. The most common interview rounds that candidates reported facing are:

1. Behavioral or “Googleyness” Round: This round focuses on evaluating your fit within the company culture and assessing your behavioral attributes. Expect questions about your past experiences, contributions, and the alignment of your values with Google's culture. Most questions you face will take either an experiential form i.e. “tell me about a time you faced xyz”; or a situational form “How would you handle xyz situation”. Generally speaking; our recommendation is that for both types - try to find real world examples of situations you’ve faced; craft them into “stories”; and use these to deliver key messages that demonstrate your alignment with Google’s key values.

Some sample interview questions for this round:

  • Google values a culture of continuous learning and growth. Can you provide an example of a time when you took the initiative to learn a new skill or expand your knowledge in a particular area of data science?
  • Describe a situation where you faced tight deadlines or competing priorities. How did you manage your time effectively and ensure the successful completion of the project?
  • How do you stay updated with the latest trends and advancements in the field of data science? Can you provide an example of how you applied new knowledge or techniques to improve a project or solve a problem?

    2. Stats and Probability Round: In this round, you can expect a range of heavily technical statistics and probability questions. The focus will be on assessing your proficiency in these areas, and you may be asked to solve complex problems and explain statistical concepts in detail.

Some sample interview questions for this round:

  • How will you generate a matrix that follows Bernoulli distribution in python? Divide each element of the matrix with the sum of columns.
  • Explain what is meant by the terms "parametric" and "non-parametric" in statistics, and describe the key differences between these two approaches?
  • 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?

3. Product Interpretation Round: The specific focus of this round may vary depending on the domain in which you are applying as a Data Scientist. For example, if you are applying in the advertising domain, you can expect questions related to advertising subject matter. The emphasis may be more on market knowledge rather than computer science or algorithms.

Some sample interview questions for this round:

  • Tell me how you go about detecting a virus or inappropriate content on YouTube.
  • You have a google app and you make a change. How do you test if a metric has increased or not?
  • How would you compare if upgrading the Android system produces more searches?

4. Technical Round: This round can vary depending on the team you are interviewing for. You may encounter questions related to A/B testing, applied situations, or be asked to design a system based on provided data. 

Some sample interview questions for this round:

  • If the labels are known in a clustering project, how would you evaluate the performance of the model?
  • What is the difference between K-means and the Expectation-Maximization (EM) algorithm?
  • How to determine if a Gaussian mixture model is an appropriate choice for modeling a given data set?

5. Metrics and Experimentation Round: In this round, you can expect questions that evaluate your understanding of essential Data Science and Machine Learning concepts, such as regression and performance metrics. These questions will be focused mainly on the performance metrics part.

Some sample interview questions for this round:

  • How would you design an A/B test to compare the effectiveness of two product campaigns, given data on their performance and a goal of detecting a 3% increase in effectiveness?
  • Describe how you would make inferences from the data collected in an A/B test, including estimating the difference in campaign effectiveness and assessing the significance of the results?
  • What do you understand by precision and recall? What are the caveats to using these metrics?
  • You are working with the PM of Google Meet, who wants to roll out a new set of buttons on the UX. What advice would you give them in order to make sure they make good decisions? What metrics would you recommend tracking to assess success?

Want to practise for the Onsite Round? Book a mock interview with our Gogle Data Scientist Experts

→ Book a Mock interview here

Interview Tips to ace the Google DS Interview

When you are preparing for a Google Data Scientist Interview - we’d recommend keeping these things in mind:

  • Research about Google's company culture, values, and goals to align them with your career aspirations. Check out About Google to know more about the company culture. Be ready to talk about your previous work experience and how you have collaborated with team members and stakeholders to achieve project goals. Make sure to highlight your soft skills and behavioral traits that align with Google's values.
  • Be prepared to talk about your experience and background in machine learning, as well as your knowledge of ML frameworks and tools. Make sure to highlight your relevant skills and experiences that match the job requirements.
  • Brush up on your problem-solving skills, as you will likely face several coding problems during the interview rounds.
  • Google DS Interviews heavily focus on metrics and experimentation. Interviewers go quite deep both into which metrics you choose; so be prepared to think about the “why” and also of edge cases and proxy metrics you might want to use to drive insights. Also brush up a bit on causal inference techniques beyond raw A/B testing since this knowledge can come in very handy if you face particularly unique situations. Finally - be aware of the limitations of different techniques so you know where *not* to use them as well.

Responsibilities of a Data Scientist at Google

The responsibilities of a data scientist at Google across roles can broadly be seen as-

  • You will work with large, complex data sets. Conduct end-to-end analysis that include data gathering and requirements specification, exploratory data analysis (EDA), model development, and written and verbal delivery of results to business partners and executives.
  • You will be required to partner with internal teams to scope, build and deliver strategic initiatives driving all things marketing data, with a focus on digital marketing data pipelines. (This responsibility is specific to a DS working in the advertising domain)
  • Finding ways to combine large-scale experimentation, statistical-econometric, machine learning, and social-science methods to answer business questions at scale is a key responsibility of a DS at Google.
  • Identify patterns and behaviors that are effective predictors of performance and critical drivers for a successful plan. For example, analyze historical data and identify key user engagement metrics that correlate with successful advertising campaigns.
  • Use causal inference methods to design and suggest experiments and new ways to establish causality, assess attribution, and answer strategic questions using data. For instance, design and analyze controlled experiments to assess the causal impact of new product features or marketing initiatives on user behavior and business outcomes.
  • DSes at Google will be asked to deliver a data-driven approach, based on a people-based marketing strategy, to build, segment, and test audiences.

Skills and Qualifications needed for Data Scientists at Google

Here are some skills and qualifications that will help you excel in your Data Science interviews at Google. One thing to note here is that the degree qualification (bachelor’s/ master’s) is different for every role.

  • It's important to be able to effectively articulate and translate business questions into statistical analyses using available data. This skill will help you derive meaningful insights and arrive at data-driven answers.
  • Having experience with machine learning on large-scale datasets is crucial. This includes familiarity with advanced techniques for handling and analyzing big data efficiently.
  • Understanding causal inference methods like split-testing, instrumental variables, difference-in-difference methods, and other advanced techniques is highly valuable. Knowledge of structural econometric methods is also beneficial in understanding complex causal relationships.
  • Develop expertise in statistical data analysis, including techniques such as linear models, multivariate analysis, stochastic models, and sampling methods. These statistical methods are essential for analyzing and drawing insights from data.
  • Gain proficiency in SQL for efficient data querying and manipulation. Additionally, having experience with statistical programming languages like R or Python is important for data analysis, modeling, and visualization.
  • Familiarize yourself with experimental design principles and techniques. Also, develop expertise in supervised and unsupervised machine learning approaches for regression and classification tasks. This will enable you to build effective models and derive insights from data.
  • Develop skills in conducting root cause analysis to solve problems at both tactical and strategic levels. This involves identifying underlying causes of issues and developing data-driven solutions.

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. It's always best to check the job description and requirements on the Google's Career page before you apply for the role.

Salary Ranges

The salary range for a data scientist at Google can vary depending on factors such as location, experience, and level of seniority. On average, data scientists at Google can expect a base salary ranging from around $120,000 to $190,000 per year. However, it's important to note that this is a rough estimate and individual salaries can vary significantly based on the factors mentioned earlier.

In addition to the base salary, data scientists at Google may also receive performance-based bonuses and stock grants as part of their compensation package. These additional components can contribute significantly to the overall compensation and can vary based on individual and company performance.

Conclusion

The interview process for a Data Scientist role at Google typically includes 3 primary rounds - a recruiter screening, followed by a technical screening, and the final on-site interview round to conclude. 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. The technical screening focuses on evaluating your technical skills in statistics, probability, and basic coding. The final round is the on-site round, where you will meet multiple Data Scientists and potentially a Hiring Manager. This round typically consists of several rounds designed to assess different aspects of your skills and qualifications. Note that these rounds depend on the domain you are applying in.

Good luck with your interviews!

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