The Best Manual for Data Scientist interview at Uber 2022

Best manual for a Data Scientist interview at Uber

Uber is hiring Data Scientists in a speedy manner to support its ever expanding user base. With lots of data being generated by Uber users and drivers every second, Uber needs Data Scientists to analyse and build interpretive models that handle big quantities of data.

Because of this high demand, a data science position with Uber is an attractive career choice for any budding Data Scientist. In this blog, we will explore each step of Uber’s unique interview process.

And by the end of this you will have a good understanding of how to tackle each interview round at Uber.

What does an Uber Data Scientist do?

What do Data Scientists do at Uber?

Uber receives huge data from around the world almost every minute. A data scientist’s and analyst’s job is to figure out how to organize the chunk of data and to draft predictive models that improve the service.

The particulars of your role will depend on the team you become a part of. The various teams are: 

  • Safety
  • Platform
  • Marketing
  • Cybersecurity
  • General research

Whatever team you may be a part of, Uber hires data scientists with the knowledge and understanding of machine learning, business analytics, modeling and deep learning algorithms.

What is the Data Scientist interview structure at Uber?

The interview structure at Uber includes 4 stages which include a phone interview, technical interview, take home assignment and Onsite interview.

Uber Data Scientist Interview Rounds

#1 Phone interview

For this round, you will receive a call from a hiring manager or a recruiter. You will be questioned about your experience with technical skills and how they have shaped you as a developer

You will also discuss your interest for the particular role and what you are looking for at Uber. The recruiter will describe the role and team you are applying for, to see if you are a good fit. You will also be asked questions based on your resume.

Questions like:

  • What challenges did you face with a previous project?
  • What makes you want to work at Uber?
  • How have your previous experiences prepared you to contribute to Uber?

Pro Tip: Make sure to highlight your personal contributions in past projects.

#2 Technical interview

After making it through the first round, you will receive a call from an Uber Data Scientist this time for a round of technical questions. You will be subjected to Uber related case studies and will be asked questions based on that data.

Two areas that are important to be prepared for in this round are analytics and machine learning. This round primarily tests your problem solving and critical thinking skills for Uber-specific issues.

Pro Tip: Uber prioritizes scalability, so one must be ready to share strategies that create scalable solutions.

#3 Take home assignment

This round is a 3 part take home assignment with questions that are about data science and programming skills. You will be expected to provide longer and more deeply explained solutions for all questions.

The 3 kind of questions are: 

  • SQL and Analytics: You will be provided with a real-life problem for Uber and an SQL schema. You will then write SQL to solve analytical problems related to the real-life problem.
  • General statistics: This section contains a collection of short answer questions on metric evaluation and statistical experimentation.
  • Machine learning modeling: You will be provided with a dataset and must create a predictive model to answer a product or business problem.

The point of this round is to evaluate you in a more job focused, skill based situation.

Pro Tip: Make sure to clean your data. The interviewers are interested in the entirety of your process, not just the answer.

#4 Onsite interview

The last round of the whole process is an onsite session with 5-6 interview loops in succession. This stage is really intensive and provides you a chance to stand out from the rest. 

The interviews will mostly consist of:

  • Coding questions related to SQL or Python.
  • Interview with a data scientist with open questions on business analytics, probability and statistics.
  • An interview with a hiring manager covering deep-dive themes on Uber’s company structure and how your team fits into that structure.
  • A round with the product manager where you discuss past challenges and interpersonal problem-solving.

Pro Tip: Make sure to ask insightful questions in the end.


20 General Data Science Interview Questions

Here are some of the questions that were asked to previous interviewees at Uber:


  1. Describe binary classification. What are its real-life applications?
  2. What is caching and why is it important in data science?
  3. Find the maximum of subsequence in an integer list.
  4. Explain the steps for data wrangling and cleaning before applying machine learning algorithms.


  1. Calculate the AUC of an ROC curve.
  2. What is the purpose of sampling? What are some common types of sampling?
  3. Explain linear regression, its assumptions, and its mathematical equations
  4. What is logistic regression? How does it differ from linear regression?
  5. What metrics would you use to record the success or failure of an advertising campaign?
  6. How does your data analytics process change when working with big data?
  7. List the pros and cons of deep learning. What area of Uber would best benefit from deep learning?
  8. Can you explain the fundamentals of Naive Bayes? How do you set the threshold?
  9. Can you explain what MapReduce is and how it works?
  10. How do you detect if a new observation is an outlier?
  11. Discuss how to randomly select a sample from a product user population.
  12. How to deal with unbalanced binary classification?


  1. What would you do to summarize a twitter feed?
  2. What machine learning algorithms would best describe the likelihood of a driver accepting a ride? What assumptions are you making about the model?
  3. How does population density affect Uber’s performance? Explain the influence on both technical (how to search many drivers in a small radius) and product factors (how traffic influences costs).
  4. What factors could increase the wait time to find a driver? How would you reduce user wait time?

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