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Uber Data Scientist Interview Guide

Interview Guide May 02

The role of an Uber Data Scientist

A Uber Data Scientist is responsible for analyzing large amounts of data to help the company make informed business decisions. They use a variety of tools and techniques to collect, process, and analyze data from various sources, such as GPS data from Uber's app, customer ratings and feedback, and economic data.

One of the main responsibilities of a Uber Data Scientist is to use this data to identify patterns and trends that can be used to improve the overall performance of the company. For example, they may analyze data on driver and rider behavior to identify areas where the company can improve its matching algorithm or optimize pricing strategies. They also use data to identify new opportunities for growth and expansion, such as identifying cities where Uber is not yet available but where there is a high demand for ride-hailing services.

In addition to analyzing data, a Uber Data Scientist also plays a key role in developing and implementing new data-driven products and features. For example, they may work with engineers and product managers to design and build new data-driven features such as Uber's "destination filter" which allows riders to set a destination before requesting a ride.

Uber hires Data Scientists across the company and there are different seniority levels depending on the scope and expected impact. They have Senior and Staff level roles and some openings for Data Science Managers along with some Applied Scientist roles.

Overall, the role of a Uber Data Scientist is to use data and analytics to drive business growth and improve the overall user experience for Uber's riders and drivers.

How to Apply for a Data Scientist Job at Uber?

To apply for a Data Scientist job at Uber, browse the job listings on Uber's career website and find the data scientist position that best matches your qualifications and experience. Once you have found a position that you are interested in, you will be able to submit an application online. However, we would highly recommend taking the referral route if you know someone in the company as it increases your chances meaningfully. One tip regarding your resume - make a few tweaks for the position and the role you are applying for which will help you have a better chance compared to other candidates. For instance, some DS roles might explicitly call for a background in ML; others might need you to be very good at visualization - these are exactly the sort of things you should then highlight if you have past experience doing. If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters will give you feedback on your resume.

It's important to note that the application process may vary depending on the position and location, so you should always check the specific job listing for more information on the application process.

Responsibilities of a Data Scientist at Uber

The responsibilities of a data scientist at Uber across roles can broadly be seen as-

  • Creating practical data insights and solutions from Uber’s large datasets.
  • Employing a mastery of statistical analysis, including descriptive statistics, correlation, regression, and confidence intervals.
  • Designing metrics and developing SQL queries to generate reports.
  • Generating ideas for exploratory analysis for a range of use cases. For instance to shape paid marketing strategies.
  • Use machine learning, experimentation, causal learning, time series analysis, natural language processing, and more to develop and lead statistical and machine learning efforts for different support systems of Uber.
  • Conducting feasibility studies to analyze the impact of proposed product changes.
  • Communicating your findings to cross-functional peers and management.

Skills and Qualifications needed for Data Scientists at Uber

Here are some of the skills and qualifications that may be required for a Data Scientist at Uber. One thing to note here is that the degree qualification (bachelor’s/ masters’) is different for every role.

  • It's mandatory to have at least 5+ years of experience in some Data Science roles.
  • Make sure you have advanced SQL expertise. This will be crucial for working with the large and complex data sets that are common in this role.
  • A basic understanding of causal inference, experimental design (such as A/B experiments) and statistical methods will be important for being able to analyze data effectively and guide teams on making data-informed decisions.
  • Ability to extract insights from data and summarize learnings and takeaways. This will be important for communicating your findings to stakeholders and leadership.
  • Experience with Excel and some form of dashboarding or data visualization tool (such as Tableau, Mixpanel, Looker, or similar) is a plus as it will help you to effectively present your findings.
  • Having experience launching productionized machine learning models at scale will give you an edge over the other candidates.

Uber Data Scientist Interview Guide

As a part of the Uber Data Scientist interview, the candidate will need to go through multiple interview rounds:

1. Phone Screening - The first round is to have a quick discussion about your work experiences and the roles you’ve had in the past company. The standard phone screen is typically taken by a Recruiter from HR.

2. The second round of interviews consists of multiple rounds of interviews with current data scientists from Uber. Each round consists of interviews with Uber staff. Some candidates reported facing more than one interviewer during these rounds. Here you will face questions involving live coding, previous relevant work that you have done and conceptual questions about what Uber does.

3. The third and final round is a virtual onsite interview. This interview is usually with a Hiring Manager. The questions asked here will be related to the case studies to be assessed on the spot.

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Uber Data Scientist: Phone Screening


The goal of the Phone Screening round is to have a quick discussion about your past work experience and the roles you’ve had in your previous companies. The standard phone screen is typically taken by a Recruiter from HR. Some candidates reported getting a take home challenge after this round. The take home challenge is often related to building a ML model - although some analytics focused roles instead needed candidates to perform a set of tasks related to analyzing a data set.

Interview Questions

  • Why do you want to join Uber?
  • Why do you think you will be a good fit for the role?
  • What responsibilities do you expect to have from your job at Uber?
  • What makes you the best candidate for this position?

Uber Data Scientist: Second Round of Interviews


The second round of interviews consists of multiple rounds of interviews with current data scientists from Uber. Each round consists of interviews with Uber staff. Some candidates reported facing more than one interviewer during these rounds. Let’s look at some of the example rounds:

  1. An interview round with a Product Manager where you are asked questions on product intuition and product impact. Here the interviewer will check whether you are able to communicate with a non-technical audience.
  2. A coding round with questions related to Python or R. You could also face some SQL questions as well. For instance questions related to data manipulation, and data structures and algorithms.
  3. An interview round with a Data Scientist team leader. Here you will be asked questions related to metrics and models you’d consider using for specific use cases. For the ML questions in this round, we’d recommend learning about some time series models given the temporal nature of the data.

Please note that these are just some of the examples of the rounds you could face. However, the rounds can be different for different roles and positions.

Interview Questions

  • Describe conceptually how you would decide on how to price rides if you were to do it from scratch?
  • What are type I & type II errors? Which one is worse?
  • What are the basic assumptions of A/B testing? Do you have experience in A/B testing?
  • Explain the LSTM algorithm.
  • What are the applications of Bayes’ Theorem?
  • How to handle imbalanced data?
  • Explain p-value.
  • Describe regression to an executive with no background in statistics.
  • Write code to simulate the roll of a die, given only a uniform distribution (continuous) random number generator.

Uber Data Scientist: Virtual Onsite Interview


The third and final round is a virtual onsite interview. This interview is usually with a Hiring Manager. The questions asked here will be related to the on-the-spot case studies given to you. This round is usually the final round where you are tested on your problem-solving skills and approaches. Some candidates reported facing experimental questions. For instance, questions related to driver oriented metrics and shipper side metrics.

Interview Questions

  • What affects Uber ride requests? How would you predict ride requests?
  • What are the most important metrics for Uber?
  • Suppose there is a button that we put in the Uber app that suggests riders to download and use Uber Eats, how do we measure this button's efficacy?
  • Suppose we use push notifications to convert Uber riders to Uber Eats users, how do we determine what kind of people get annoyed by the push notifications?
  • How do you come up with an experiment to test a new policy?

Check out video guide that delves into the interview process and provides valuable tips tailored to each round of the interview.

Interview Tips

When you are preparing for a Uber Data Science interview - we’d recommend the following things to keep in mind:

  • It is important to have a strong, comprehensive understanding of  causal inference and A/B testing as it was a common theme for several candidates.
  • When applying for a DS Manager, leadership qualities are very essential and will be highly valued. Therefore, when talking about previous DS projects - make sure to talk through the role you played in actively driving forward progress, impact etc - since your hiring manager is going to be keen to extract a signal around your leadership skills.
  • Read about the business fundamentals behind Uber and how they use Data Science to expand on their tools. Think in advance of the different angles of their marketplace; the needs of the users; and how you’d measure health and impact across these needs.
  • Be clear with your technical knowledge required to fit into the specific role you’re interviewing for at Uber (most roles require you to index more heavily on specific aspects of the craft).


The interview process for a Data Scientist role at Uber typically includes 3 primary rounds - a phone screening, second round of interviews, and the virtual onsite interview. During the phone screening, the interviewer will assess your qualifications, experience and alignment with the role. The second round of interviews will consist of multiple rounds with Product Managers, Data Scientists and some coding rounds. The virtual onsite interview will usually be with a Hiring Manager where you will be asked questions related to the given live case studies.

Good luck with your interviews!

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