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Data Scientist Manager Interview Questions

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61 data scientist manager interview questions shared by candidates

Example probability brain teasers: probabilities when rolling two dice twice with the second roll depending on the outcome of the first, the German tank problem, breaking a 4-digit code with different prior information etc. In total: know your combinatorics and Bayes rule in-and-out! Example business cases: - You own a museum and consider putting up an exhibition by a new artist. How would you go about the decision? What kind of information / data would you look for, how would you use it? - You want to buy a TV on amazon.com. How do you make a decision? - You performed A/B testing on a change in a search engine algorithm. Although in B, the coders made a mistake which led to really poor search results, user clicked on more results as well as on more ads. How do you interpret the results, what do you do next? etc. In total, they look for how you think and approach a problem. They often want to know how you deal with incomplete information, short timelines, trade-offs. Overall, all interviewers were very friendly and professional. My HR contact was very responsive throughout the whole process. My interview process was unusually long because of visa questions - normally they are faster than 3 months;-)
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Data Scientist (Product Intelligence Manager)

Interviewed at Yammer

3.9★
Apr 18, 2014

Example probability brain teasers: probabilities when rolling two dice twice with the second roll depending on the outcome of the first, the German tank problem, breaking a 4-digit code with different prior information etc. In total: know your combinatorics and Bayes rule in-and-out! Example business cases: - You own a museum and consider putting up an exhibition by a new artist. How would you go about the decision? What kind of information / data would you look for, how would you use it? - You want to buy a TV on amazon.com. How do you make a decision? - You performed A/B testing on a change in a search engine algorithm. Although in B, the coders made a mistake which led to really poor search results, user clicked on more results as well as on more ads. How do you interpret the results, what do you do next? etc. In total, they look for how you think and approach a problem. They often want to know how you deal with incomplete information, short timelines, trade-offs. Overall, all interviewers were very friendly and professional. My HR contact was very responsive throughout the whole process. My interview process was unusually long because of visa questions - normally they are faster than 3 months;-)

1: Trees-based methods are sometimes called greedy algorithms. Can you explain what greedy means, and then explain what is a potential downside of using a greedy algorithm? 2) Can you explain the concept of multicollinearity and how it can impact a model? Then discuss how you measure the amount of multicollinearity. Finally, using the measure you identified, how much multicollinearity would cause concern for a model, and why? 3) A colleague approaches you with an idea to run a marketing mailer experiment. The colleague wants to test the impact of two different marketing mailers (let’s call them Mailer A and Mailer B) on life insurance sign-ups. The colleague asks you: “how many mailers do I need to send out to have a statistically significant sample?” How do you respond? What questions would you ask the colleague to help answer their question? 4) Assume you deployed a binary classification model and after 12 months your business partner asks you to verify the model is still performing as expected. What steps would you take to validate the deployed model’s performance? What different recommendations would you give your business partner if the model is performing as expected versus if the model is showing deteriorated performance? 5) Please share what you like about the open role. 6) Can you tell us about your prior mentoring or leadership experiences? 7) why statefarm .
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Manager for Data Scientist Interns (Lead Data Scientist)

Interviewed at State Farm

3.4★
Mar 17, 2025

1: Trees-based methods are sometimes called greedy algorithms. Can you explain what greedy means, and then explain what is a potential downside of using a greedy algorithm? 2) Can you explain the concept of multicollinearity and how it can impact a model? Then discuss how you measure the amount of multicollinearity. Finally, using the measure you identified, how much multicollinearity would cause concern for a model, and why? 3) A colleague approaches you with an idea to run a marketing mailer experiment. The colleague wants to test the impact of two different marketing mailers (let’s call them Mailer A and Mailer B) on life insurance sign-ups. The colleague asks you: “how many mailers do I need to send out to have a statistically significant sample?” How do you respond? What questions would you ask the colleague to help answer their question? 4) Assume you deployed a binary classification model and after 12 months your business partner asks you to verify the model is still performing as expected. What steps would you take to validate the deployed model’s performance? What different recommendations would you give your business partner if the model is performing as expected versus if the model is showing deteriorated performance? 5) Please share what you like about the open role. 6) Can you tell us about your prior mentoring or leadership experiences? 7) why statefarm .

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