Google Data Scientist Interview Questions

John H.

John H.

I love iced coffee, cute pictures of dogs, and SQL. I've previously worked at Big Tech as a data analyst and now spend my time writing and helping job seekers ace their big tech interview @ bigtechinterviews.com.

Are you preparing for a Google Data Scientist interview? Congratulations!

A data scientist role at Google is one of the most sought-after positions in the tech industry. This is because data scientists at Google are responsible for some of the most important projects at the company, such as developing algorithms to improve search results, improving Google Maps, and developing new features for products like Gmail and YouTube.

To land a data scientist role at Google, you must ace the interview process. This article will cover the Google Data Science interview process, common questions across behavioral, real technical questions and answers, and ML/AI, and even share tips to ace your interview to help you prepare.

Overview of the Data Science Interview

The Google Data Science interview process typically consists of a series of technical and behavioral interviews with current data scientists, engineers, and hiring managers. The goal is to assess the candidate’s technical skills, problem-solving abilities, and cultural fit within the company.

The interviews are usually conducted in a virtual setting or on-site at one of Google’s offices. They can range from 45 minutes to an hour long and can be conducted individually or in a panel format.

Generally, you can expect the following format:

  • Initial phone discussion: A 30-minute conversation with a recruiter from Google, focusing on your background and skills.

  • Technical assessment: A one-hour video conference with a Google Data Scientist, aimed at evaluating your technical abilities.

  • Onsite: Spend a full day at Google, engaging in interviews with Data Scientists and various team members. These sessions will explore your knowledge in machine learning, statistics, and data analytics.

  • Leader interview: A brief 30-minute session with a Google leader, aimed at understanding your career aspirations and how well you mesh with the team’s dynamics.

Now that we have an overview of the interview process, let’s examine the key qualifications and responsibilities of a Google Data Scientist.

Key Qualifications and Responsibilities

Minimum Qualifications:

  • Bachelor’s degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.

  • 5 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL).

  • Experience in machine learning or generative, predictive, or attribution modeling.

Preferred Qualifications:

  • Master’s degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.

  • Experience in Intelligent Tutoring Systems (e.g., Bayesian Knowledge Tracing, Item Response Theory).


  • Gather, synthesize, and translate business requirements into scalable solutions to address business needs.

  • Own and drive complex technical projects from planning through execution following SDLC principles.

  • Perform analysis using relevant tools (e.g., SQL, R, Python), utilizing comprehensive technical knowledge and custom data infrastructure.

  • Own the process of gathering, extracting, and compiling data across sources, ensuring quality through validation and restructuring.

  • Report Key Performance Indicators (KPIs) to support business reviews and translate analysis results into actionable insights.

  • Build and prototype analyses and business cases iteratively to provide insights at scale, developing a comprehensive knowledge of Google data structures and metrics.

Data Science Interview Process at Google

In this section, we will discuss the different interview rounds Data Science candidates should expect to encounter. 

Round 1: Initial Phone Discussion

The first step of the process is a phone discussion with a Google recruiter. This call typically lasts about 30 minutes and allows the recruiter to get to know you better, understand your experience and qualifications, and determine if you are a good fit for the role. With proper preparation, your recruiter can become your strongest internal supporter once interviews conclude and the hiring team decides to extend an offer.

Round 2: Technical Assessment

The technical assessment round is a one-hour video conference with a current Google Data Scientist. This round evaluates your coding skills, statistical knowledge, and problem-solving abilities. During this round, you may be asked to complete a coding challenge or whiteboard questions.

Round 3: Onsite Interviews

If you pass the initial phone discussion and technical assessment rounds, you will be invited to an onsite interview at one of Google’s offices. This round typically involves four to five sixty-minute interviews with different data science team members.

Round 4: Leader Interview

The final round of interviews involves a brief 30-minute session with a Google leader. This interview aims to understand your career aspirations and how well you align with the company’s culture.

Data Science Behavioural Interview Questions

Behavioral questions are a staple in the interview process for a role at Google, especially for positions in data science. These queries aim to gauge not just how effectively you can think and react in real-time but also whether you fit within Google’s dynamic culture and can articulate complex ideas simply. When formulating your responses, touching upon four key aspects Google values highly is crucial.

Google evaluates job candidates based on four main criteria. First is general cognitive ability, focusing on quick learning and adaptability. Second is role-specific knowledge, where your skills and experience are matched with the job requirements. Third, leadership qualities are important, especially the ability to collaborate and lead when necessary. Lastly, “Googlyness” assesses your fit within Google’s unique work culture, including comfort with ambiguity, proactivity, and teamwork.

  • Why Google? This question is your opportunity to showcase your passion for the role and the company. Mention specific aspects of Google’s data science culture that excite you or discuss how Google’s commitment to continuous learning and skill expansion aligns with your career aspirations.

  • Describe a Data Science Project: When discussing past projects, focus on what made them successful. Highlight key metrics or the positive changes your work enabled, demonstrating the impact and value you bring.

  • Managing Multiple Projects: A common question is how you prioritize tasks. Google wants to see your approach to balancing various responsibilities without compromising quality.

  • Career Goals: Discuss your professional aims and how you plan to achieve them. Be specific about how a role at Google fits into your career path and how you intend to grow within the company.

  • Favorite Google Product: Having a favorite Google product and articulating why it impresses you can show your genuine interest in the company. Ensure you’re familiar with a broad range of Google’s offerings, but be prepared to discuss a few select products in detail.

  • Learning from Failure: Be prepared to discuss a project that didn’t go as planned. Openness about failures and insights into what you learned and how it informed your approach to future projects can demonstrate resilience and the ability to evolve.

Preparing for these behavioral questions by reflecting on your experiences and aligning your answers with what Google values most will prepare you well for your data science interview.

Data Science Technical Interview Questions

In addition to behavioral questions, you can also expect technical questions in your data science interview at Google. These questions are designed to test your technical knowledge and problem-solving skills. Here are some common technical interview questions you may encounter:

Google SQL Interview Questions:

How would you calculate the median in SQL?

  • There are many ways to calculate the median in SQL, but one approach is to use a combination of the COUNT and RANK functions. First, count the total number of values in the column using COUNT, then rank each value using RANK (with an ORDER BY clause). Finally, select the value with a rank equal to half of the total count.

How would you handle missing values in a dataset using SQL?

  • One approach to handling missing values is to use the CASE statement. This allows you to set specific conditions for replacing missing values with another value, such as zero or the average of the column’s non-missing values.

What’s the difference between using a UNION and a UNION ALL in SQL?

  • The main difference between using a UNION and a UNION ALL in SQL is that UNION automatically removes duplicate records from the results, whereas UNION ALL includes all duplicates. UNION performs a distinct operation on the results by default, which can be useful when you want to ensure that all returned rows are unique. On the other hand, UNION ALL does not perform any duplicate removal, making it faster in cases where you know the datasets do not overlap or when duplicate records are needed in the result set.

How do you find the third highest mountains in MX and USA?


					SELECT "country", "name"
 SELECT "country", "name", RANK() OVER (PARTITION BY "country" ORDER BY "height" DESC) as "rank"
 FROM mountains
 ) as m
WHERE "rank" = 3
ORDER BY country ASC
					SELECT b.genre, SUM(o.order_amount) AS revenue
FROM books AS b
JOIN orders AS o ON b.id = o.book_id
WHERE o.order_date >= '2022-01-01' AND o.order_date <= '2022-12-31'
GROUP BY b.genre
ORDER BY revenue DESC;
					with ranks as (
,rank() over (
partition by customer_id, order_datetime::date
order by order_datetime asc
) myrank
from bids
,bid_id first_bid
from ranks
where myrank=1
order by customer_id asc;

Python Programming Interview Questions:

How do you improve the performance of a Python script?

  • Improving the performance of a Python script can involve a variety of strategies, such as using more efficient data structures (e.g., using sets instead of lists for membership tests), leveraging list comprehensions and generator expressions, and utilizing built-in functions and libraries which are often optimized for performance. Additionally, the use of modules like NumPy for numerical tasks or implementing multiprocessing or threading to exploit parallelism can significantly enhance script performance.

How you would implement a machine learning model in Python?

  • Implementing a machine learning model in Python typically begins with data preprocessing, including cleaning, normalization, and feature selection. Next, one would select a model based on the problem type (e.g., regression, classification) and use a library like scikit-learn to train the model on the preprocessed data. This involves splitting the data into training and test sets, fitting the model to the training data, and then evaluating its performance on the test set. Fine-tuning the model parameters may be required to optimize performance.

What are decorators in Python, and how would you use them?

  • Decorators in Python are a powerful and expressive tool for modifying the behavior of functions or methods. They allow you to wrap another function in order to extend its behavior without permanently modifying it. Decorators are commonly used for logging, access control, memorization, and code timing. To use a decorator, you simply precede the definition of a function with the decorator’s name preceded by the @ symbol.

Data Science Statistical and Probability Interview Questions

In your data science interview at Google, expect to encounter questions that challenge your understanding of statistical theories and their application in real-world scenarios. Here are some example questions and insightful answers to help you prepare:

Explain the significance of the p-value in hypothesis testing.

  • The p-value plays a critical role in hypothesis testing; it measures the probability of obtaining observed results, or results more extreme, assuming the null hypothesis is true. A p-value lower than a predetermined threshold (usually 0.05) indicates a statistically significant difference, leading researchers to reject the null hypothesis. This statistical measure helps in determining the significance of the results derived from a data set.

How would you use linear regression to predict future trends?

  • Linear regression is a foundational tool in predictive modeling, enabling the prediction of a dependent variable based on one or more independent variables. By establishing the best-fit linear relationship between the variables, it provides a formula that can be used to predict future values. This method is widely used in forecasting, where past data is analyzed to predict future occurrences.

What is Bayes’ Theorem and how is it applied in data science?

  • Bayes’ Theorem is utilized to update the probabilities for hypotheses as more evidence or information becomes available. It’s a vital component in the toolkit of a data scientist for predictive modeling and risk assessment. By incorporating prior knowledge, data scientists can refine their predictions and analyses, making Bayes’ Theorem critical for decision-making processes in data science.

How can the Central Limit Theorem be used in data analysis?

  • The Central Limit Theorem (CLT) is fundamental in data analysis as it allows for the approximation of the sampling distribution of the sample mean to be normal, regardless of the population’s distribution, provided the sample size is sufficiently large. This theorem underpins many statistical methods and enables analysts to make inferences about population parameters from sample statistics, hence its crucial role in data analysis efforts.

Each of these questions aims to assess your comprehension and ability to apply statistical concepts and methodologies in a data science context. Demonstrating a clear understanding of these principles can markedly highlight your qualifications for a data science position at Google or within the tech industry at large.

Data Science Product & Business Case Interview Questions

Preparing for a product and business case interview at Google requires strong analytical thinking and problem-solving skills. Here are some example questions that may be asked, along with possible approaches to answering them:

You are given a data set that contains information about when users click on ads. How would you use this data to optimize ad campaigns?

  • Utilizing user click data, you can analyze user preferences, engagement times, and the effectiveness of different ad creatives. By developing a predictive model or performing A/B testing, strategies to optimize ad placements, timings, and creatives can be devised, enhancing campaign performance and ROI.

You are given a data set that contains transaction data from an eCommerce website. How would you use this data to increase conversion rates?

  • Analyzing eCommerce transaction data provides insights into customer buying behavior, popular products, and potential bottlenecks in the sales funnel. By employing statistical modeling or machine learning, personalized recommendations, targeted promotions, and an optimized checkout process can be implemented to improve conversion rates.

Given a data set, how would you find the most important factors contributing to customer churn?

  • Identifying factors contributing to customer churn involves analyzing customer data for correlations and patterns related to churn. Techniques like decision trees, logistic regression, or gradient boosting can reveal significant predictors of churn, facilitating targeted retention strategies.

How would you develop a pricing strategy for Friday night rides for a rideshare app?

  • For an Uber-type app, developing a dynamic pricing model for Friday night rides involves considering factors like demand and supply, traffic patterns, events in the city, and historical data. Initially, a simple regression model can be used for baseline pricing. Over time, as more data becomes available, the pricing strategy could be refined using machine learning algorithms, like random forest or neural networks, to incorporate real-time data such as ride requests, weather conditions, and competitor pricing.

How would you investigate if teenagers are leaving Facebook because their parents are joining?

  • To investigate whether teenagers are leaving Facebook due to parental presence, an analysis would involve combining data sets of user information (focusing on teenagers and their parents) and usage patterns. Using SQL to correlate teenager activity levels inversely with the increase in parent sign-ups could offer insights. A typical SQL query could merge user demographic data, parent-child linkage, and usage activity to form a foundational dataset for this analysis.

How do you create a dashboard to monitor customer usage and engagement?

  • Creating a customer usage dashboard entails defining key engagement metrics like session length, visit frequency, and actions per visit. By segmenting these metrics over defined time periods (daily, weekly, etc.), businesses can glean insights into user behaviors and peak activities. Aggregating this data via SQL queries, the dashboard can guide strategies to enhance content delivery and user interaction to boost engagement.

How can customer satisfaction be measured using Google Maps data?

  • To quantify customer satisfaction on Google Maps, an analytical approach involving sentiment analysis of reviews and statistical examination of rating trends is recommended. Exploring additional engagement metrics, such as repeat usage and interactions with app features, can provide a comprehensive view of user satisfaction levels and areas for improvement.

What approach would you take to analyze usage patterns and improve engagement on Yelp?

  • For a Yelp check-in app, understanding usage necessitates examining check-in patterns, reviews post-check-in, and visit frequency. Employing data mining to identify trends like peak check-in times and popular locations, and correlating check-ins with positive reviews, can highlight features driving engagement. Further, using machine learning techniques like clustering algorithms can segment users by behavior, facilitating targeted marketing efforts and feature development to enhance user experience.

Data Science ML/AI Interview Questions

How would you develop a machine learning model to predict whether or not a customer will purchase a product?

  • To develop a machine learning model for predicting customer purchases, you’d start by collecting and preprocessing relevant data, such as past purchase history, customer demographics, and engagement metrics. Training a model on this dataset with a binary outcome (purchase or not) enables the identification of patterns and factors influencing purchase decisions.

What is a decision tree?

  • A decision tree is a type of machine learning model used to predict the value of a target variable by splitting the dataset into smaller subsets until each subset contains only one data point.

How would you use a decision tree to predict whether or not a customer will churn?

  • To predict customer churn using a decision tree, you would need to train the model with data on customers who have previously churned. This involves analyzing customer behavior and attributes to identify patterns. Once trained, the model can predict the likelihood of future customers churning.

What is gradient boosting?

  • Gradient boosting is a machine learning algorithm designed to improve accuracy by training a series of weak models sequentially. Each model corrects errors made by the previous ones, combining their predictions to form a more accurate final prediction.

How would you use gradient boosting to improve the accuracy of a machine learning model?

  • Improving a model’s accuracy with gradient boosting involves training weak models on different data subsets and iteratively adjusting them based on the accuracy of the previous model’s predictions. This process helps in reducing bias and variance, leading to a more accurate and robust model.

Tips for Acing Your Google Data Scientist Interview 

  • Be prepared to solve common SQL problems and other technical questions. The first part of the interview will likely be focused on your technical skills, so you should be prepared to answer questions about SQL, machine learning, and data analysis.

  • Be prepared to solve problems. The second part of the interview will be focused on your problem-solving abilities. You should be prepared to solve problems that are based on real-world data sets.

  • Practice your interviewing skills. In addition to practicing your technical skills, you should also practice your interviewing skills. This means being able to clearly and concisely communicate your thoughts and ideas.

  • Be yourself. The best way to ace any interview is to simply be yourself. Google is looking for candidates who are smart, creative, and passionate.

Final recap for the Google Data Science Interview

These are just some examples of questions you may be asked in the second part of the Google Data Scientist interview. Be sure to come up with your own solutions to these questions before the interview so that you will be prepared to impress the interviewer. Don’t forget to also read our recent article covering the latest Google SQL interview questions and check out our free and paid plans on app.bigtechinterviews.com if you’re looking to learn and/or pass the SQL interview. Google Data Scientists are among the best in the world at what they do, so you will need to be at the top of your game to land this job. Good luck!

Additional Resources

Frequently Asked Questions

Here are some common questions candidates have regarding Google Data Scientist interviews:

What level of proficiency in SQL is required for a Google Data Scientist position?

  • Google expects a strong proficiency in SQL for data scientist roles. Candidates should be comfortable with advanced SQL queries, including joins, subqueries, window functions, and the ability to handle large datasets efficiently.

How significant is machine learning knowledge for securing a data scientist role at Google?

  • Machine learning knowledge is crucial for a data scientist role at Google. Candidates should have a solid understanding of various machine learning algorithms, their application, and how to tune them to solve specific problems.

Can I apply for a Google Data Scientist role without a degree in Computer Science or related fields?

  • Yes, you can apply for a data scientist role at Google without a degree in Computer Science. Google values diverse educational backgrounds, provided you have the necessary skills in data analysis, machine learning, and programming.

How can I prepare for the behavioral interview segment?

  • For the behavioral part of the interview, focus on the “STAR” method (Situation, Task, Action, Result) to structure your responses. Highlight your ability to work in teams, your problem-solving capabilities, and how you’ve dealt with failure or ambiguity in past projects.

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