Uber Data Scientist Interview Questions & Answers
Below is a list of our Uber, Inc. interview questions. Click on any interview question to view our answer advice and answer examples. You may view 5 answer examples before our paywall loads. Afterwards, you'll be asked to upgrade to view the rest of our answers.
Accomplishment
1. 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.
How to Answer
Organizations generally hire workers for one reason: their ability to contribute to attaining the company's business objectives. This question is meant to determine if you understand your role's impact on the organizations you work for. It will also help the Uber interviewer learn about your contributions to your previous employers. You should provide specific examples and quantify the benefits.
Answer Example
"Data Science has had a profound impact on businesses and their decision-making process. This practice helps businesses make quicker and more accurate decisions, communicate their products' benefits better, encourage innovation and explore new ideas. In my current role, I was involved in a prototyping project that employed data science methodology to quickly investigate new revisions of the software we were developing without taking the time to write, debug, and test the code. This led to us determining the best path to take to develop the software our customers needed and would be willing to pay for and reduce the development cycle by over 50%."
Behavioral
2. A a Data Scientist, how do you employ statistics to analyze data and develop business recommendations?
How to Answer
Data Scientists use a variety of tools, statistics being one of the most used and commonly employed. An interviewer will ask this question early in the interview to set the stage, learn more about your skills and experience, and guide you toward other, more specific questions. Keep this in mind when responding to this question because it will provide you with the opportunity to move the interview in a direction that you are comfortable with and can easily address.
Answer Example
"Statistics is probably one of the strongest tools a Data Scientist has in their arsenal. It helps us to identify patterns, find hidden insights, and quickly analyze large data sets. Statistics provide information about consumer behavior, interests, engagement, and other aspects of the shopping and purchase process. They also allow for the rapid development of models that validate assumptions and inferences."
Behavioral
3. Can you describe some of the steps you take to ensure that a regression model fits the data?
How to Answer
This is an example of a technical question. As a data scientist interviewing with Uber, you can anticipate that most of the questions you will be asked during a job interview will be technical. Technical questions should be answered succinctly and directly, with no embellishment. You should also anticipate that the Uber interviewer will ask follow-up questions to learn more about the topic or clarify your answer.
Answer Example
"One of the key steps I take to ensure that the regression model fits the data is employing the R-squared methodology. This addresses the relative measure of fit. Another is to use the F1 score to evaluate the null hypothesis. One last methodology is a root-mean-square error, or RMSE, which provides the absolute measure of fit."
Behavioral
4. How do you deal with an unbalanced binary classification when analyzing a data set?
How to Answer
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.
Answer Example
"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."
Behavioral
5. 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?
How to Answer
This is another example of a Behavioral Question, and you can use the STAR framework to organize your answer. The interviewer is interested in learning about your communication skills and style. Walk them through your answer systematically, describing how you communicate complex issues in a clear and non-technical manner.
Answer Example
"While Data Science is a complex and highly technical field, the people who use the information I provide are usually experts in fields other than data. Therefore, when I present my findings, I work hard to communicate them in terminology the audience is familiar with, focusing on the conclusions and recommendations rather than the data, statistics, and analysis methodology. This approach results in the organization attaining the business objective of the analysis. I also prepare myself to answer questions about the science behind the analysis if necessary."
Behavioral
6. Do you perform data wrangling and data cleaning before applying machine learning algorithms to your data analysis?
How to Answer
This is an operational question. The Uber interviewer will ask operational questions to learn more about how you go about doing your job. One of the key responsibilities of a data scientist is to ensure that the data sets they are using are appropriate for the analysis they are performing. Data wrangling and cleaning are two processes used to accomplish this. You should be familiar with these and able to explain them. As with any operational question, keep your answer direct and to the point and anticipate a follow-up question or two.
Answer Example
"I believe it is important to perform both data wrangling and cleaning before applying any machine learning algorithms I will use here at Uber. This will ensure that the data set is appropriate. They are the data sets I intended to work with for my analysis. The standard deviations must meet the study guidelines, the relationships between the data are valid, and the data is normalized and standardized. This eliminates any outliers or variables that would potentially skew the results I obtain."
Behavioral
7. 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?
How to Answer
By asking this question, the Uber interviewer is revealing that their organization deals with unstructured data and needs to hire someone who has experience with this and can organize this type of data to make it usable. Your answer must include a specific example of how you accomplished this in a previous role to prove your ability to do it again.
Answer Example
"Unstructured data is difficult, but not impossible to work with while performing an analysis. The key is to utilize tools and techniques designed to effectively analyze this type of data. On a recent project, I was tasked with helping the sales team improve its customer relationship management process. I utilized a combination of a NoSQL database and Amazon's Simple Storage Service (S3) to collect and analyze the data, producing the results the sales team needed."
Creative Thinking
8. 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?
How to Answer
The Uber interviewer may ask this type of question to learn more about you as a person vs. exploring your technical skills. People hire people, and one of the primary purposes of an interview, besides confirming your qualifications for the job, is to determine if you would be a good fit for the organization. This question will provide some insight into both your personality and your ability to communicate your ideas.
Answer Example
"Being immersed in the data is one of my favorite parts of being a Data Scientist. I often discover things I didn't expect or wasn't even looking for. Once, while parsing data to determine how consumers shop for a new car, I discovered that the most important criteria most people used was color. Respondents to the survey we use listed this more often than make, model, features, or even price. This enabled the organization to redesign its marketing campaign to emphasize the variety of color options available for its vehicles. I anticipate discovering similar trends about Uber's business if selected for this position."
Creative Thinking
9. 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?
How to Answer
This is another question meant to help the Uber interviewer determine your level of creativity and initiative. These are qualities not typically associated with Data Scientists but which are key to the results they produce. Organizations like Uber value employees who are willing to work independently with little supervision and are confident enough to be responsible for their actions. The best way to respond to this question is with confidence and a straightforward answer.
Answer Example
"One of the most rewarding parts of my job is the ability to work on a project with few parameters and the freedom to explore and experiment. I was recently assigned to determine why sales of one of our products had fallen off. I decided to approach the problem from two perspectives: those of the sales team and the customers. Using text analysis, data visualization, and other analytical techniques, then comparing the results from the two groups, I determined that the issue was one of communication. I recommended that the sales team modify their messaging and follow up with the customers more quickly. This helped to reverse the fall-off in sales."
Creative Thinking
10. Can you discuss some of the weaknesses of a linear analysis model?
How to Answer
The Uber interviewer is asking another technical question, but this time in a back-handed manner. By asking you to discuss the negative aspects of the topic, they anticipate that you will demonstrate your knowledge by providing both the negative and positive aspects. Be sure to stay as positive as possible when you answer this question. Being negative will reflect poorly on you, even though you were asked to discuss the shortcomings.
Answer Example
"While it is an effective methodology, there are also several drawbacks to a linear analysis model. One issue is that the linear analysis model makes strong assumptions that may not apply to the application used. Also, there is an assumption of a linear relationship, normality between the variables, minimal multicollinearity, and homoscedasticity. Finally, a linear model cannot be used for discrete or binary outcomes. As long as you avoid these pitfalls, the linear analysis model can be beneficial to a data scientist."
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.
How to Answer
Data Science and analysis of complex data sets is a very technical discipline. However, Uber's stakeholders who use the results of the analysis need to be able to clearly understand what the data is telling them and use it to improve their operations and help them make business decisions. You need to present your work in a manner that is easy to understand and utilize. This is known as data visualization. The Uber interviewer is seeking to understand how you organize and present the data to accomplish this objective.
Answer Example
"While the process of analyzing data is important, it is critical that the results of the analysis be useful to the stakeholders here at Uber. It is important to understand what the stakeholder's objectives are when deciding how to present my results. One method I've found to be effective is to add graphs, pictures, and illustrations to my presentations and reports. Once, when presenting an analysis of customer usage trends for a product our organization sold, I incorporated images of the product and animated them to expand in size relative to the growth in customer adoption, adding the statistics and actual growth numbers. The audience remarked about how clear this made the information."
Creative Thinking
12. 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.
How to Answer
Data Science, by its nature, is a disciplined practice with little opportunity for creativity or changes. However, organizations like their employees to take the initiative and innovate to improve processes, with the goal of reducing costs or increasing the outcomes of actions they take. When preparing for an interview, you should have a few stories available which demonstrate initiative and out-of-the-box thinking.
Answer Example
"During one of my previous jobs, I was on a team which focused on customer satisfaction scores. We determined this by analyzing data involving product returns, repeat purchases, customer referrals, and text analysis. While performing this work, I noticed a correlation between customer satisfaction and a specific feature of one of our products. I knew the feature couldn't be altered, but I thought that if it was highlighted in the product documentation and suggestions for its use emphasized, the customers would use it more often. I recommended this to the product manager who implemented my idea. The result was an increase in the use of the feature and a corresponding reduction in customer complaints."
Education
13. Can you describe how Data Analysis is used by businesses and other organizations like Uber?
How to Answer
While this appears to be another Technical Question, it is more of a General Question. The Uber interviewer is likely to ask this early in the interview to help establish a conversational tone and develop some avenues to follow-up questions. As with any interview question, your answer should relate to the company's operations and how you believe they use data analytics to run their business. You can usually determine this from the information provided on their website and in the job posting.
Answer Example
"As a Data Scientist, I've come across many examples of how businesses like Uber use data analysis to improve the results of their operations. For example, eCommerce firms can use data analysis to understand customer behavior, reduce churn, and better target marketing. Financial organizations use it to evaluate investment opportunities and detect fraud. Healthcare companies employ data analysis to develop treatments for specific groups of patients."
Education
14. What are some of the differences between a histogram and a box plot?
How to Answer
The interviewer at Uber is asking a technical question, which requires you to compare different types of visual models used to analyze data. Knowing the differences between two similar but different techniques used to illustrate the results of data analysis will confirm that you are qualified for this role. Technical questions like this are best answered by comparing the terms presented by the interviewer and possibly providing an example of how they are used in your profession. Your answer should also be brief and to the point to provide the interviewer the opportunity to ask a follow-up question.
Answer Example
"Boxplots and histograms are both visualizations used to illustrate a data distribution that communicates information in different ways. Histograms are bar charts that illustrate the frequency of a numerical variable's values. I use this to understand the shape of the distribution, the variation, and any potential outliers that may skew the data. While boxplots don't illustrate the shape of the distribution, they enable you to view information such as the quartiles, the range, and outliers. I believe boxplots are more useful than histograms when comparing multiple charts and would favor this methodology if hired by Uber."
Education
15. 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?
How to Answer
In many fields, people become compliant the longer they are in the field, and they tend not to stay abreast of developments in the field. However, Data Science is a relatively new and quickly developing industry, and Data Scientists must stay abreast of emerging technologies and methodologies. You should be able to demonstrate your curiosity and efforts to keep up-to-date on developments in the industry. You also need to make sure you can discuss the technologies you mention if the interviewer asks you any follow-up questions.
Answer Example
"Since our field is moving so quickly, it is imperative to learn about new developments and technologies which will help me do my job better. Key trends I'm currently following include Augmented Analytics, Continuous Intelligence, Explainable AI, and Data Fabrics. The one technology I'm most interested in is Blockchain. While this is most associated with cryptocurrency, there are many other applications for blockchain technology, and the applications for this are still in their infancy."
Experience
16. What is a decision tree, and how would you use this in your job as a data scientist here at Uber?
How to Answer
Data scientists use various tools and methodologies to accomplish their tasks and ensure the results they produce are accurate. You should be able to discuss these with authority during your Uber interview for a data scientist job. You may have also recognized this as a technical question. This is a typical structure of a technical question - asking you to define a term and provide an example of how it is used in your profession. Your answer should address the definition first, then provide an example of how you would use this item in your job.
Answer Example
"Decision trees are graphical models used to illustrate the options available and the choices made during a decision process. A decision tree is intuitive and easy to build. However, it lacks accuracy, so it should only be used to illustrate the process. Like a tree, it begins with a base or trunk and then grows. Each decision option is called a node. The last decision options are at the top of the tree and are known as leaves."
Experience
17. What are some of the assumptions required to accurately perform a linear regression analysis?
How to Answer
As a data scientist, you use many different methodologies to analyze the data sets you are working with at Uber. Often these methodologies involve several steps, assumptions, or other items. A common practice during an interview is to provide the Uber interviewer with a list of items in your answer. Organize your answer and make sure that none of the items are repeated in your response.
Answer Example
"There are several assumptions that I use when performing a linear regression analysis. Some of these include:
o Ensuring that the data I use in the sample is representative of the population
o Determining if the variance of the residual is the same for any value of X
o Confirming that the relationship between X and the mean of Y is linear
o Reviewing the observations to ensure that they are unique and independent of each other"
Experience
18. 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?
How to Answer
This is a technical question, the purpose of which is to determine your familiarity with and knowledge of software used by data scientists. The Uber interviewer is also interested in learning whether you are adept at working with their organization's software and tools. The best way to answer this question is to first state the names of the software you have used and are familiar with. Then, make the point that most statistical analysis software products have similar features and that you've been able to easily transition from one to another one when necessary.
Answer Example
"In my current position, we use Tableau for the majority of our work. We also have licenses for Statgraphics and JMP Statistical Software, but these are only used in circumstances where their unique features are more suited for the task at hand. I've also used Salesforce Analytics Cloud and MATLAB in previous roles. If Uber uses different software, I've found that transitioning to a new software analytics tool is relatively easy due to the similarity in the features and user interface between the different packages."
Experience
19. 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?
How to Answer
This is a technical question asking for both the definition of the term and an explanation of how you will use it in your work as a data scientist at Uber. During an interview, you should make sure you always listen carefully to the complete question. Many candidates will begin formulating their answers as soon as the interviewer begins asking the question. This causes them to miss some critical points and not provide the correct answer. A useful technique to counter this is to pause for two seconds before answering the Uber interviewer's question. This also ensures you will not 'step on' the interviewer when they are still talking, which is a critical mistake during an interview (or any conversation).
Answer Example
"As a data scientist, I use cross-validation to assess how well the analysis model I am using will perform on a new and independent dataset. A typical way to use cross-validation is to split the data into two sets. You then use one data set to build the model and the second one to test your analysis. This helps to improve the accuracy of and my trust in the results of the analysis."
Experience
20. What is Data Cleansing and why is it important in Data Analysis?
How to Answer
Technical questions like this one are straightforward ways for the Uber interviewer to explore and confirm your technical competencies related to the position for which they are interviewing you. Your preparation for an interview with Uber should include researching and practicing technical questions, in addition to general and behavioral questions. Always answer technical questions succinctly, without embellishment or additional information.
Answer Example
"Data cleansing is the process of ensuring that data obtained from a wide variety of sources is suitable for analysis. It involves a high-level review of the data set, detecting any anomalies or inaccuracies, and correcting these to ensure the data is correct and accurate. It can also be used to eliminate data components that are irrelevant to the analysis being performed."
Experience
21. 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?
How to Answer
This is a Technical Question, which seeks to determine your technical capabilities and knowledge of data scientists' standard tools. By specifying these tools, the Uber interviewer is indicating that these are what Uber uses and expects you to be competent in. You should compare these and state their purpose in analyzing data, even if you don't regularly use them.
Answer Example
"SAS, R, and Python are probably the most commonly used tools for data analytics. SAS has a wide array of functions, a user-friendly graphical interface, and strong reporting features. R's strength is that it is an open-sourced tool and is widely used in academic and research environments. Python is also an open-sourced product but is more widely used and supported. It is easy to learn and interfaces well with other tools. The best part about Python is its large portfolio of libraries and modules."
Experience
22. 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?
How to Answer
The purpose of this question is to gain a broader picture of your background and experience. In addition to performing the tasks related to the job you are interviewing for, organizations prefer to hire individuals who can expand the role of the job to accomplish other organizational objectives. They are also interested in your fit and how you may improve the company culture.
Answer Example
"When I was young and still in school, I didn't have the goal of becoming a data scientist. However, I was naturally curious and enjoyed learning new things. I also liked solving problems, especially using numbers and sets of data. One of my first jobs was in a library where I had to reshelve books. I quickly learned that if I spent some time organizing the books before I began replacing them on the shelves, I could reduce the time the job took. I challenged myself to continually beat my previous times by devising new ways to organize the books and navigate the library. I tracked this and charted my progress. This made a routine job more interesting and enjoyable. If hired by Uber, I can apply this same strategy to make my and my co-workers' jobs more engaging."
Experience
23. What experience do you have conducting text analytics? Describe a project you worked on that required text analytics.
How to Answer
This is another technical question the interviewer will ask to confirm your skills and experience as a Data Scientist. They want to ensure that you are qualified for the job and are familiar with a specific process that they use to analyze data to improve the results of their operations.
Answer Example
"Text Analytics is the process of creating meaning out of written communications. A common usage of this is in a customer experience context, examining text written by, to, or about customers. This helps find patterns and topics of interest and enables the organization to take action based on what it learns. While working on a recent project involving reviewing a service our organization provides, I examined the email communications between our support team and the customers. My analysis identified a specific issue that customers inquired about frequently. We then reviewed the documentation related to this and realized that it was vague and somewhat confusing. After updating the information and performing subsequent text analysis, I confirmed that the number of customer inquiries about this issue had dropped by 70%."
Experience
24. 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.
How to Answer
Keep in mind that organizations hire people to help them achieve their objectives. This question seeks to learn about the past contributions you made in your previous positions and explore whether these are similar to the challenges their company is facing. You should understand the issues the company routinely faces from your pre-interview research. Make sure your answer to this question aligns with the needs of the employer.
Answer Example
"In my most recent role, I introduced my team to new data visualization techniques to enable us to perform the data analysis faster and more accurately. It also enabled us to communicate our findings to the other business stakeholders in a manner which they could easily understand and relate to."
Experience
25. 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?
How to Answer
The purpose of this question is to explore your knowledge and experience with machine learning. The interviewer may want to confirm your skills in this area and your direct knowledge of the machine learning methodologies their company utilizes. Be prepared to provide a concrete example and the rationale behind using the methods you chose.
Answer Example
"Much of my experience with machine learning is in the area of medical imaging. Our team employed machine learning methodologies including classification, clustering, and regression analysis to help improve the accuracy of the assessment of the images our equipment produced."
Experience
26. 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.
How to Answer
This is a Behavioral question, which the Uber interviewer uses to determine how you dealt with a specific issue in the past and how you would deal with it if you reencountered it working for Uber. Behavioral questions are best answered using the STAR format. This stands for Situation, Task, Action, and Results. Using this format helps you organize your answer and lead the interviewer through your story systematically.
Answer Example
"In my current position, we performed analysis on a large set of data to determine why the company didn't achieve the results they expected from a major initiative they had implemented (Situation). Management had a hypothesis and expected the analysis to confirm this (Task). We completed the analysis, but the data led us to reach a different conclusion (Action). My colleagues and I repeated the analysis but used a modified data set and different analytical tools, and this time the results matched the original assumptions. However, when management implemented changes based on this study, the results were the same as before (Results). What I learned is to be true to the methodology and let the results speak for themselves so the problem can be accurately identified and addressed."
Experience
27. What data visualization tools do you have experience using? Which one is your favorite to use and why?
How to Answer
This question is similar to the one about statistical software programs. It is attempting to discover your technical knowledge and familiarity with the tools the company you are interviewing with uses. Again, the best response is a direct one, stating your understanding of software tools, your preference, and the reason for your opinions.
Answer Example
"Data visualization tools I use include Google Charts, Tableau, Grafana, Chartist.js, FusionCharts, Datawrapper, Infogram, ChartBlocks, and D3.js. I prefer Tableau because it offers a variety of visualization styles, is easy to use, and can handle large data sets. I like Tableau because their help desk is very responsive and open to suggestions from the user community. It isn't a true open-source software, but the product is continuously being improved by the developers and the company's customers."
Experience
28. 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?
How to Answer
This is another technical question geared toward determining your hard skills and qualifications for the data scientist position. This is relatively easy to answer since you need these skills and experience to work in this industry. Your answer should be honest, straightforward, and brief. This Tick-the-Box question is required but likely won't differentiate you from other candidates.
Answer Example
"I'm experienced in the majority of the major programming languages including Python, R, Java SQL, C++, and Scala. Of these, I prefer Python due to its applicability to a wide variety of tasks and its general acceptance in the data science community. I also like that it is an open-source language, and the syntax is easy to understand. I understand that your team here at Uber also prefers Python. Is this true?"
Personal
29. Do you follow the hypothesis that many small decision trees are more accurate than one large one?
How to Answer
This is an example of a follow-up question that the Uber interviewer asks to expand on a previous question you answered. You should always anticipate follow-up questions during an interview. Keeping your answers short and to the point will encourage the interviewer to ask follow-up questions before moving on to the next question. This is beneficial if you have in-depth knowledge about a topic and want to spend some time on it.
Answer Example
"No. I believe just the opposite. The larger a decision tree is, the more accurate it is as a decision process model. Small decision trees can cause problems because the options are limited, and the model may not fit the problem. If possible, I would create a model that looks more like a forest than a tree with multiple options and a distinct direction to help you navigate the woodland."
Personal
30. In your opinion, is mean square error a good or bad measure of model performance?
How to Answer
As with any career or profession, the techniques and methodologies used may differ between individuals. Interviewers at Uber will ask you questions similar to this one to learn more about your expertise and how you go about doing your job. They also are interested in your opinion on specific topics, some of which may be controversial. When you answer this type of question, it is best to give them your honest opinion. Trying to please the Uber interviewer may work during the interview, but it may cause issues once you are hired.
Answer Example
"I believe that the mean square error, or MSE, is a flawed measure of a decision model's performance. The problem of using MSE for this purpose is that it weighs larger errors more than smaller ones. This results in applying emphasis on the large deviations in the data. I prefer to use mean absolute deviation, or MAE, which is a more robust model and provides a more accurate measure of a model's performance."