Uber Data Scientist Mock Interview

Practice 30 Uber Data Scientist interview questions covering experimentation, machine learning, and product analytics.

Question 28 of 30

How do you deal with an unbalanced binary classification when analyzing a data set?

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William Swansen has worked in the employment assistance realm since 2007. He is an author, job search strategist, and career advisor who helps individuals worldwide and in various professions to find their ideal careers.

This is yet another operational question asking you how you react to a specific situation during a data analysis exercise. You should be able to answer this question easily as an experienced data scientist. Your answer should address the use of metrics and how they impact the analysis. Knowing this and explaining it to the Uber interviewer will reinforce your qualifications for a data scientist's position.

William Swansen

"The easiest way to address an unbalanced binary classification is to review the metrics you are using in your model. Even though some metrics may be accurate, they may skew the results. Another way you can neutralize this issue is to increase the impact on the analysis for incorrectly classified and any minority class data. This results in a superior model, which produces more accurate results. Another solution is to oversample some of the minority class data or under-sample some of the majority class data, which will balance the binary classification."

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30Uber, Inc. Interview Questions & Answers

Experience

1.What statistical software programs do you have experience using in past positions in this field? Which one do have you the most experience with or feel the most confident using?

Experience

2.Here at Uber, we use several programming languages to create our software. What programming languages do you have experience using? Of these, which do you have the most experience with? Which do you have the least experience with?

Education

3.To be a successful Data Scientist, many in the industry believe it is important to keep up-to-date on the newest technologies and methodologies. What new data-related technology/methodology have you heard of that you wish you could learn more about?

Accomplishment

4.The work of a Data Scientist can have a large impact on the strategy, and ultimately success, of Uber's business. Is there a time you felt your work as a Data Scientist impacted your company's strategy development? Explain your role and contribution.

Creative Thinking

5.When your job requires you to be immersed in data, you can discover some interesting patterns or trends. What is the most interesting learning you discovered through the mining/exploration of data?

Experience

6.What data visualization tools do you have experience using? Which one is your favorite to use and why?

Experience

7.Describe to me a data project you worked on in the past that you would do differently with the knowledge/experience you have acquired up to this point and/or new technology that was not available at the original time of the project.

Behavioral

8.Describe a time when you had to present findings to a non-technical audience (very little or no background in data or databases). What strategies did you use to ensure the audience did not get confused and clearly understood the message?

Experience

9.Uber is in the process of implementing machine learning in our applications. Describe to me your experience with machine learning methods. Is there a particular method you have more experience with than others?

Experience

10.In your past positions, have you had experience contributing to the improvement of data analysis processes, database management, data infrastructure, or anything along those lines? If so, please explain your contributions.

Creative Thinking

11.Data visualization is an important skill that will be used often here at Uber when communicating results with stakeholders. Describe to me one of your most innovative data visualization ideas that went beyond pie and bar charts.

Experience

12.What experience do you have conducting text analytics? Describe a project you worked on that required text analytics.

Experience

13.How have past positions, unrelated to data analysis, helped you in your current profession as a Data Scientist? How will this help you to be successful here at Uber?

Creative Thinking

14.Many companies rely on Data Scientists to tell them what analysis is possible with the data available. Talk about a time when you took the initiative to recommend a new business measure for the company to track.

Behavioral

15.Describe a project where you had a surprisingly difficult time dealing with unstructured data. How did you overcome the obstacles and what tools did you use?

Creative Thinking

16.Data Scientists do a lot of exploring and testing of hypotheses. Tell me about a time where you were given freedom to explore a business problem with very few parameters. What was your initial approach in attacking this project?

Experience

17.Here at Uber, we use several programming languages to create our software. Can you compare SAS, R, and Python programming tools and describe their use in Data Analytics?

Behavioral

18.A a Data Scientist, how do you employ statistics to analyze data and develop business recommendations?

Experience

19.What is Data Cleansing and why is it important in Data Analysis?

Education

20.Can you describe how Data Analysis is used by businesses and other organizations like Uber?

Behavioral

21.Can you describe some of the steps you take to ensure that a regression model fits the data?

Experience

22.What is a decision tree, and how would you use this in your job as a data scientist here at Uber?

Personal

23.Do you follow the hypothesis that many small decision trees are more accurate than one large one?

Personal

24.In your opinion, is mean square error a good or bad measure of model performance?

Experience

25.What are some of the assumptions required to accurately perform a linear regression analysis?

Behavioral

26.Do you perform data wrangling and data cleaning before applying machine learning algorithms to your data analysis?

Creative Thinking

27.Can you discuss some of the weaknesses of a linear analysis model?

Behavioral

28.How do you deal with an unbalanced binary classification when analyzing a data set?

Education

29.What are some of the differences between a histogram and a box plot?

Experience

Uber Data Scientist Mock Interview

How do you deal with an unbalanced binary classification when analyzing a data set?

30Uber, Inc. Interview Questions & Answers

1.What statistical software programs do you have experience using in past positions in this field? Which one do have you the most experience with or feel the most confident using?

2.Here at Uber, we use several programming languages to create our software. What programming languages do you have experience using? Of these, which do you have the most experience with? Which do you have the least experience with?

3.To be a successful Data Scientist, many in the industry believe it is important to keep up-to-date on the newest technologies and methodologies. What new data-related technology/methodology have you heard of that you wish you could learn more about?

4.The work of a Data Scientist can have a large impact on the strategy, and ultimately success, of Uber's business. Is there a time you felt your work as a Data Scientist impacted your company's strategy development? Explain your role and contribution.

5.When your job requires you to be immersed in data, you can discover some interesting patterns or trends. What is the most interesting learning you discovered through the mining/exploration of data?

6.What data visualization tools do you have experience using? Which one is your favorite to use and why?

7.Describe to me a data project you worked on in the past that you would do differently with the knowledge/experience you have acquired up to this point and/or new technology that was not available at the original time of the project.

8.Describe a time when you had to present findings to a non-technical audience (very little or no background in data or databases). What strategies did you use to ensure the audience did not get confused and clearly understood the message?

9.Uber is in the process of implementing machine learning in our applications. Describe to me your experience with machine learning methods. Is there a particular method you have more experience with than others?

10.In your past positions, have you had experience contributing to the improvement of data analysis processes, database management, data infrastructure, or anything along those lines? If so, please explain your contributions.

11.Data visualization is an important skill that will be used often here at Uber when communicating results with stakeholders. Describe to me one of your most innovative data visualization ideas that went beyond pie and bar charts.

12.What experience do you have conducting text analytics? Describe a project you worked on that required text analytics.

13.How have past positions, unrelated to data analysis, helped you in your current profession as a Data Scientist? How will this help you to be successful here at Uber?

14.Many companies rely on Data Scientists to tell them what analysis is possible with the data available. Talk about a time when you took the initiative to recommend a new business measure for the company to track.

15.Describe a project where you had a surprisingly difficult time dealing with unstructured data. How did you overcome the obstacles and what tools did you use?

16.Data Scientists do a lot of exploring and testing of hypotheses. Tell me about a time where you were given freedom to explore a business problem with very few parameters. What was your initial approach in attacking this project?

17.Here at Uber, we use several programming languages to create our software. Can you compare SAS, R, and Python programming tools and describe their use in Data Analytics?

18.A a Data Scientist, how do you employ statistics to analyze data and develop business recommendations?

19.What is Data Cleansing and why is it important in Data Analysis?

20.Can you describe how Data Analysis is used by businesses and other organizations like Uber?

21.Can you describe some of the steps you take to ensure that a regression model fits the data?

22.What is a decision tree, and how would you use this in your job as a data scientist here at Uber?

23.Do you follow the hypothesis that many small decision trees are more accurate than one large one?

24.In your opinion, is mean square error a good or bad measure of model performance?

25.What are some of the assumptions required to accurately perform a linear regression analysis?

26.Do you perform data wrangling and data cleaning before applying machine learning algorithms to your data analysis?

27.Can you discuss some of the weaknesses of a linear analysis model?

28.How do you deal with an unbalanced binary classification when analyzing a data set?

29.What are some of the differences between a histogram and a box plot?

30.Can you define cross-validation and describe how you will use this process when analyzing a data set if hired to work here at Uber?