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1. Started with a detailed explanation of a past project - what was the business question, how did you come up with the solution, what was your hypothesis, how did you design the A/B test, why did you make certain choices, what was the result etc. Prepare 1-2 examples from your past, where you can talk in depth about the technical elements of your project. 2. Let's say we have a dataset with attributes for a house (Sq footage, locality etc) and house price. How will you predict the house price from these attributes? (Build a multiple regression model) 3. For this multiple regression model, explain the end-to-end process. What steps will you take before building the model, how will you impute missing values, how will you handle outliers etc. What are the underlying assumptions of a regression model? 4. Once the model is built, how will you infer the relationship (sign and magnitude) between the house attributes and house price. How will you explain it to someone that's not a technical person? 5. For the regression coefficients, how will you interpret them, (p-values, confidence interval etc). How will you explain a p-value to a layman 6. Next question was about "how will you segment customers" in order to serve a business requirement, such as determining which customers to show a given ad (I answered with clustering, because the business problem wasn't very specific, he just described it very generally) 7. For clustering, how does it work, how to choose the value of K in k-means. I also said we can use Gaussian mixture models for clustering, which he didn't seem to know because he asked me to clarify what I mentioned. There might have been a few more questions that I don't remember, but the theme of the interview was to check how well you know the basics of Stats/ML. I believe I answered most of the questions correctly so to receive the feedback that I wasn't up to the mark technically seemed like a case of Google not wanting to reveal the real reason, whatever it was. Either way, make sure you confirm the format of the interview with the recruiter. Because I was already interviewing with other companies, I had brushed up on my Stats/ML basics, but you might not be as lucky. Good luck!
avatar

Marketing Data Scientist

Interviewed at Google

4.4★
Nov 19, 2020

1. Started with a detailed explanation of a past project - what was the business question, how did you come up with the solution, what was your hypothesis, how did you design the A/B test, why did you make certain choices, what was the result etc. Prepare 1-2 examples from your past, where you can talk in depth about the technical elements of your project. 2. Let's say we have a dataset with attributes for a house (Sq footage, locality etc) and house price. How will you predict the house price from these attributes? (Build a multiple regression model) 3. For this multiple regression model, explain the end-to-end process. What steps will you take before building the model, how will you impute missing values, how will you handle outliers etc. What are the underlying assumptions of a regression model? 4. Once the model is built, how will you infer the relationship (sign and magnitude) between the house attributes and house price. How will you explain it to someone that's not a technical person? 5. For the regression coefficients, how will you interpret them, (p-values, confidence interval etc). How will you explain a p-value to a layman 6. Next question was about "how will you segment customers" in order to serve a business requirement, such as determining which customers to show a given ad (I answered with clustering, because the business problem wasn't very specific, he just described it very generally) 7. For clustering, how does it work, how to choose the value of K in k-means. I also said we can use Gaussian mixture models for clustering, which he didn't seem to know because he asked me to clarify what I mentioned. There might have been a few more questions that I don't remember, but the theme of the interview was to check how well you know the basics of Stats/ML. I believe I answered most of the questions correctly so to receive the feedback that I wasn't up to the mark technically seemed like a case of Google not wanting to reveal the real reason, whatever it was. Either way, make sure you confirm the format of the interview with the recruiter. Because I was already interviewing with other companies, I had brushed up on my Stats/ML basics, but you might not be as lucky. Good luck!

There is a pool of people who took Uber rides from two cities that were close in proximity, for example Menlo Park and Palo Alto, and any data you could think of could be collected. What data would you collect so that the city the passenger took a ride from could be determined? If some type of supervised classification algorithm was used to split the two populations, for example Support Vector Machine, what type of business decision would you make on points that appear close to the hyperplane?
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Data Scientist

Interviewed at Uber

3.7★
Apr 8, 2015

There is a pool of people who took Uber rides from two cities that were close in proximity, for example Menlo Park and Palo Alto, and any data you could think of could be collected. What data would you collect so that the city the passenger took a ride from could be determined? If some type of supervised classification algorithm was used to split the two populations, for example Support Vector Machine, what type of business decision would you make on points that appear close to the hyperplane?

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