**Table of Contents**

**Table of Contents**

## Introduction

The Google data science interview questions comprise of both behavioral and technical problems. Most of the behavioral questions are centered around how the candidate fits in with Google's work culture. The technical questions span multiple topics in data science knowledge.

## Google Behavioral Interview Questions

The behavioral interview questions usually occur in the recruiter screen and throughout the onsite interview.

Questions in the recruiting screen would be like *would you like to work with small or large teams* or *what direction you see your career moving in.*

**Example Behavioral Interview Questions**

- Describe a past data science based project.
- How do you sort priorities when engaged in multitasking.

## Google Technical Interview Questions

Google asks about 5+ different topics in data science spanning machine learning, statistics & probability, product & business, and coding.

Generally there is a **high emphasis in statistics and coding interview questions**. Many times they ask a question that combines both concepts. Read more about statistical coding questions here. Or learn more about the statistics and A/B testing portion of the interview here.

### Machine Learning

*What is the difference between K-mean and EM?**Why use feature selection? 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 function of p-values in high dimensional linear regression?*

### Statistics & Probability

*For a sample size of N, the margin of error is 3. How many more samples do we need for the margin of error to hit 0.3?**What is the assumption of error in linear regression?**How can you tell if a given coin is biased?**Explain how a probability distribution could be not normal and give an example scenario.**You have a deck and you take one card at random and guess what the card is. What is the probability you guess right?**What is the difference between parametric and non-parametric testing?*

### Product & Business

*How would you detect inappropriate content on Youtube?**How do you test if a new feature has increased engagement in Google's ecosystem?**If the outcome of an experiment results in one group clicking 5% more than the other, is that a good result?*

Check out our strategies for tackling product data science interview questions

### Programming

*Write a function to generate N samples from a normal distribution and plot the histogram.*

## Google Data Science Onsite Interview

The data science onsite interview panel will look like this:

**Business case study:**Questions are mainly case-study type involving real-life Google problems. The interviewer may ask you to then write a query to analyze the business case study using SQL.**Applied statistics and ML interview:**This interview covers statistical concepts and modeling questions as well as a related coding question.**Product Metrics:**This interview covers a product case study. It will be a deep dive into a product and how to analyze success of a feature or debug what might be happening in the data.**Leadership and Product Sense Interview:**This interview assesses your leadership skills. The aim here is to understand how you leverage your communication and decision-making to influence others.**Googlyness Interview:**This interview is basically about how well you work with others, help team members achieved team goals, how you can navigate workplace ambiguity, and how well you can work under pressure

### Notes and Tips

- It is of worth to note that Google questions are
**standardized and rely heavily on situational scenarios with their products**. Study Google's large breadth of products and understand how you would personally improve or test them. - Google’s data science interview aims to determine the level of domain knowledge you possess how you could provide business-driving insights. Brush up on your knowledge of statistics and probability given these questions can be some of the hardest to solve.
- There are four general attributes that Google looks for in candidates. First is the
**general cognitive ability**, which screens based on how candidates can learn and adapt to new situations. The second is**role-related knowledge**which is based on background, skillsets, and experience that are specific and relevant to the roles. The third is the**leadership**attribute. Google’s core culture is about building a team of high performers individuals who are great team players and can one day step into leadership roles. The fourth and last attribute is the**Googlyness**, to ensure candidate succeed in their roles. Google assesses on “comfort with ambiguity”, “bias to action”, and a “collaborative nature” [1]. - Google at its core has an employee-focused culture. It has a corporate culture that motivates employees to share information cross-functionally to support innovation that enables it to maintain its competitiveness. This ecosystem ensures that every employee maintains competitiveness and innovativeness through training and informally through personalized leadership and management support.

## Example Google Data Science Interview Question and Solution

**Question: **

Given three random variables independent and identically distributed from a uniform distribution of 0 to 4, what is the probability that the median is greater than 3?

**Solution:**

If we break down this question, we'll find that another way to phrase it is to ask what the probability is that **at least two of the variables are larger than 3.**

For example, if look at the combination of events that satisfy the condition, the events can actually be divided into two exclusive events.

**Event A:** All three random variables are larger than 3.**Event B:** One random variable is smaller than 3 and two are larger than 3.

Given these two events satisfy the condition of the median > 3, we can now calculate the probability of both of the events occuring. The question can now be rephrased as **P(Median > 3) = P(A) + P(B).**

Let's calculate the probability of the event A. The probability that a random variable > 3 but less than 4 is equal to 1/4. So the probability of event A is:

**P(A) = (1/4) * (1/4) * (1/4) = 1/64**

The probability of event B is that two values must be greater than 3, but one random variable is smaller than 3. We can calculate this the same way as the calculating the probability of A. The probability of a value being greater than 3 is 1/4 and the probability of a value being less than 3 is 3/4. Given this has to occur three times we multiply the condition three times.

**P(B) = 3 * ((3/4) * (1/4) * (1/4)) = 9/64**

Therefore the total probability is P(A)+P(B) = 1/64 + 9/64 = **10/64**