Google Data Scientist Interview Questions To Get You Hired

Google data science interviews are challenging.  The data scientist interview questions are tricky, specific to Google’s data products, and cover a wide range of data science and machine learning concepts. The good news is that the right preparation can make a big difference and get you hired at one of the FANG companies. If you’re interviewing for a data scientist role at Google or you’re just curious about what a data scientist interview at Google looks like - we’ve broken down the various stages of the Google data scientist interview process that will help you prepare and do your best. 

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Data Science at Google 

Google Search Engine, Google Maps, YouTube, Google Analytics, Google Ads, and Google Translate are some of the popular applications and services by Google. Data Science is at the heart of Google’s business model. Google Maps, for instance, is extensively used and has more than 5 Billion downloads. Similarly, the search engine processes over 40,000 search queries per second. 

You must have seen automated content recommendations based on your browsing history on Google Chrome. Google Maps also show recommendations based on your current and previous locations. Google Fit, the fitness tracker by Google, generates alerts as per the fitness goals mentioned. Data analytics combined with machine learning algorithms and Artificial Intelligence (AI) technologies predict the data patterns and trends in these applications, and there is no surprise that Google has mastered it.

What does a data scientist at Google do?

Google is a market leader in the tech space and you will get to develop and apply your skills in data analysis, data handling, and management as a Data Scientist. As a Data Scientist, you will work will cross-functional teams to enhance Google’s products and services. You will apply forecasting, analytical, and statistical methods to improve the product/service lifecycle. 

You will get to work on massive scalability and storage solutions at Google. You can explore new platforms and services with the application of diverse data science technologies combined with other emerging technologies, such as the Internet of Things (IoT), AI, etc. 

You may land up in either of the following teams as a Data Scientist at Google: 

• Engineering and Design: You will apply advanced analytical models to the data sets to develop the data and business insights. You will work with different teams to provide data-driven business-impact suggestions. 

• Google-tech Professional Services: You will work on technical optimizations to assist the clients in achieving their business objectives. 

• Geo: You will implement advanced analytical models on the high-volume Geo data sets and analyze them to develop recommendations at various levels. 

• Ads: You will develop and expand the advertising capabilities of the organization using statistical analytics and the implementation of ML algorithms. You may also be a part of the search ads team requiring you to work on complex experiments to determine the scope of improvements. You will work with other software teams and analysts to make sure customer engagement and experience levels improve. 

• Operations and Support: You will work with the analysts and engineers to analyze and interpret the information to improve customer operations and support. 

• Google Maps Core Metrics: You will create core metrics and experiments to determine Google Maps’ engagement and retention schemes. You will also work on the measurement and testing aspects. 

• Business Strategy: You will work on the internal development and strategy-making of the organization. You will conduct data analytics on Google’s business information to determine the business needs and scope for improvement. 

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Google Data Scientist Job Description - All You Need to Know

Here’s how a typical data scientist job description for Google looks like -

• Work with massive data sets with gathering, processing, and analysis of the data pieces.

• Application of advanced analytical methods on the data sets 

• Build-up improved knowledge of Google data structures and metrics 

• Manage and prototype analysis pipelines iteratively 

• Data visualisation and displays of qualitative and quantitative information 

• Predictive and description analysis on the data sets 

• Application of forecasting and optimisation methods 

Skills Required for a Data Scientist at Google

Technical Skills 

• Qualifications: Minimum Master’s Degree in fields as Statistics, Applied Mathematics, Computer Science, Information Technology, or other relevant branches of Science & Engineering 

• Work Experience: Google prefers candidates with some relevant work experience in programming, data handling, engineering, or statistics. 

• Statistical Language: You should have basic – intermediate knowledge of at least one statistical language, such as R or Python. 

• Data Language: SQL is the most popular data language. You can also develop skills in MySQL or JavaScript. 

• Data Visualization: It will be one of the most critical tasks for you to perform as a Data Scientist. You can have the edge over the other candidates with skills and knowledge in ggplot, d3.js and Matplottlib, and Tableau. 

Soft Skills 

• Communication: You must have good communication skills and interaction abilities. As a Data Scientist, you will work with engineering and design teams, testing teams, finance teams, and others. You will also interact with the clients and stakeholders as part of your job. Communication is highly significant to understand the requirements of others and work in a team environment. 

• Adaptability: Google promotes diversity and inclusion in its workforce. You will work with team members from varied cultural and professional backgrounds. Also, you will undergo several changes in your journey as a Data Scientist at Google. You must adapt to the environment you will be put in to excel and meet the expectations. 

• Big Picture Energy: Problems and risks will be an integral part of your role as a Data Scientist. You should have the ability to work through the problems to achieve the goals identified.  

• Strategic Management: You must have the ability to think critically and strategically while working as a Data Scientist. Strategic management of the data, team, projects, and operations will be a crucial part of your role.  

Google Data Scientist Salary - How much does a data scientist at Google make?

One of the primary variables in the salary of a data scientist is the years of experience and educational qualifications of an individual. Google uses a levelling system to decide the compensation of the Data Scientists and the promotions of its employees. Though Google does not publicly announce this, many former and existing employees have confirmed the use of L coupled with a number to denote the employees’ level in the organization. 

Data science interns are at the lowermost level in terms of experience, and an intern’s salary at Google is also higher than the average data science intern salaries at other companies. A data science intern at Google can make $7,500 per month and get additional benefits, such as accommodation stipend and other health benefits. 

L3 is the entry-level data scientist, and L4 is an intermediate-level data scientist. At the entry-level, the candidates do not have any industry experience. These are usually college graduates with internships and certifications as their only experience in the field of Data Science. The average salary of the Data Scientist at the entry level is $137,786. The organisation also offers additional benefits, and these can be in the range of $20,000-30,000. The salary at the intermediate rank is usually 10-15% higher than the one offered to an entry-level candidate. 

L5 is the senior-level Data Scientist role, and it includes candidates with over five years of industry experience. L6 is the topmost level in the hierarchy, and it is the role of the Data Scientist and Manager. The resources need to have strong leadership and managerial abilities along with technical skills.  The average senior-level salary of a Data Scientist is around $161,544, with additional benefits in the range of $20,000-50,000.

How to Apply for a Data Scientist Job at Google?

You can explore multiple channels to apply for the role of a Data Scientist. You can go to the Careers page of the official website of the organisation to look for vacancies and openings. You can sign-in on the Careers page using your existing Google account or may register on the page to apply. 

You can also browse through professional social media networking channels, such as LinkedIn, to search for the available openings for the role of a Data Scientist. Currently, 1,165 data scientist jobs show up on LinkedIn for the position of Data Scientist in India. The count is 6,394 for the United States. 

You can also search for the vacancies posted by the organisation on other job portals. Google also collaborates with numerous educational institutions across the globe. College graduates can apply data science internships or entry-level data scientist role following the respective application process of their universities/colleges.

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Preparing for Your Google Data Scientist Interview

The interview process at Google is an extensive one with multiple rounds involved.

The Initial Phone Screening

The first is the phone screener, which is similar to the one followed by most of the other technology companies, such as Amazon, Facebook, and others. In phone screening, a recruiter confirms the basic knowledge, qualification, and experience of the candidate. The recruiter also checks on the willingness and availability as per the company requirements. 

The Coding/Technical Screen

The next is the technical screener. Video conferences are the platform for the process in the presence of Data Science Experts. The primary focus of this phase is on the technical skills and abilities of the candidate. You can expect interview questions from various technologies and fields, such as Statistics, Python, SQL, A/B Testing, Machine Learning, Big Data, NoSQL, etc. The data scientist interview questions in this round can range from basic to complex based on the years of experience a candidate brings to the table. 

Onsite Interview - Googleyness Screener 

If you clear the two rounds, the final is the Googleyness screener. It is primarily an on-site interview and comprises a loop of a five-step interview process. The interview panel can include Product Managers, Senior Data Scientists, Business Analysts, and others. The interviews have a mix of technical and situational questions. In the final round, the interviewers will test the leadership abilities and behavioural response of the candidate. The questions will evaluate the power of the candidate to navigate the workplace ambiguity and determine if the candidate will be a culture fit.

Top 20 Google Data Scientist Interview Questions

We have curated some of the interview questions to offer you an idea of the kinds of questions you may face in your interview for a Data Scientist at Google. 

1.  What are the steps to remove bias in A/B Testing? Also, explain the process to make an inference from data of two different advertisement campaigns. 

2. You are travelling to Switzerland and are not sure about the weather conditions. Three of your friends currently stay in Switzerland. You can contact each of these three friends before leaving for the airport. The probability that your friend is telling the truth is 1/3 and the likelihood of them lying is 2/3. All three friends tell you it is currently snowing in Switzerland? What is the probability of the correctness of this scenario?

3. If you want to build a Data Science-based product for Google, what product will you build and Why? 

4. What is the difference between supervised and unsupervised machine learning? Explain with techniques and examples under each of the two categories. 

5. List down the steps to add new Facebook members to a particular database and then code their relationship with the other members in the database. 

6. How do you test the applicability of the Gaussian mixture model? 

7. Write a program in R or Python to read a text file using a series of tweets. The output of your program shall be two text files. The first one shall have a list of all the unique words present in the tweets and the number of words that appear more than once. The second file shall have the average number of unique words in all the tweets. 

8. Why do you think NoSQL databases can be better than SQL databases? 

9. How can you develop a recommendation engine for an e-commerce website? 

10. Semi-autonomous and fully autonomous vehicles use different sensors and trackers to track the vehicle, driver, and location. How does this work? Do you see any concerns with such an approach? 

11. How will you resolve the issue of bias in the removal of missing values from a database? 

12. Can you tell the differences between the bagged model and boosted model?

13. A marketing firm wishes to enhance its brand image and reputation. How will you assist the organisation from the beginning? 

14. You have made specific changes to a mobile app for a business firm. How will you test if the changes are working correctly and give positive outcomes? 

15. Explain anomaly detection methods and techniques. 

16. What is the role of caching in Data Science? How does it work? 

17. What are the essential qualities and features of an algorithm? 

18. There are 10 red and 10 black balls in a box. Another box has 20 red and 20 black balls. You want to pick two balls at random from one of these two boxes. Which box has the higher probability of meeting your requirements? 

19. What are the Big Data frameworks and platforms you are aware of? Can you explain the Hadoop architecture? 

20. How can Data Science contribute to Smart City development? 

The interview process results are usually communicated to the candidates a few weeks later than the interview date. The recruiters take time to discuss and evaluate the candidates present in the final round of the interviews. Emails communicate the outcomes of the interview process to all the candidates irrespective of the result. The ones selected receive further communication on the joining details. The ones not selected are also informed to avoid any ambiguities. 

Top Interview Tips to Preapre for a Google Data Science Interview

• You should be well-prepared for all three rounds with an enhanced focus on the technical screening and the onsite interview.  

• Diversity in the skill-set is a key to succeed in the hiring process at Google. The sample interview questions listed above provide a glimpse of a broad range of fields covered in the interviews. It is not just the technical skills and expertise that matters in a data scientist’s recruitment process at Google. Situation-based questions, for instance, are asked in the final rounds of the onsite interviews. To answer these questions adequately, you need to have analytical and problem-solving skills. You need to have a rational mindset and critical thinking abilities. 

• You should work on technical skill development and also on strengthening your communication skills, analytical abilities, decision-making, and problem-solving skills. 

• You must have a detailed understanding and knowledge of the Google products, the business line, and the organisation timeline. 

• You may also have to answer various questions on programming and database languages. You shall begin with the basic concepts and gradually move on to the complex and advanced problems. For example, with a programming language, such as R or Python, you must first work on the syntax and commands for the particular language. You can then move to the algorithm design and development. 

• Strategy development and management is also an important area focussed in the Google interviews for Data Scientists. You will be given a hypothetical situation with questions around the problem. Such questions mainly evaluate your analytical and management capabilities. You can have such sessions with your colleagues or friends to improve your skills. 

How to Ace the Google Data Scientist Interview?

Google has a high hiring bar when it comes hiring data scientists and the best way to nail a data science interview at Google would be to demonstrate that you’ve had experience working on real-world data. Working on diverse hands-on real-world solved end-to-end data science and machine learning projects is the best way to get your hands dirty on varied datasets to demonstrate your experience.  Data Science is a fast-growing technology creating abundant job opportunities across the globe. Working as a Data Scientist for a tech leader like Google can be immensely beneficial for your career and professional development. The company also offers several on-site and additional benefits to its employees. To increase your chances of getting hired as a data scientist at Google, you must work on building a portfolio of projects that demonstrate your  technical skills and non-technical skills to create a lasting impact on the recruiting panel.

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