Cracking the Uber Data Science Interview

Dec 10, 2020 - 11 min read
Ryan Thelin
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Uber is rapidly hiring data scientists to support its growing userbase. With millions of data points being generated by Uber users and drivers every second, Uber needs data scientists to analyze and build interpretive models that handle huge quantities and variations of data.

This interesting challenge, along with a sizable wage of up to $170,000 per year, makes a data data science position with Uber an attractive career choice for any aspiring data scientist.

Today, we’ll help you prepare for your Uber interview by exploring each step of their unique interview process. By the end, you’ll know the tricks to impress interviewers every step of the way.

Here’s what we’ll cover today:


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What does an Uber data scientist do?

Uber gets millions of data points every hour from locations across the globe. Generally, your job as a data scientist and analyst is to determine how to organize the bulk of data and to draft predictive models that improve the service.

The specifics of your role will depend on the specific sector you’re applying for:

  • Safety: Reduce safety risks for users using machine learning algorithms and past incident reports.
  • Platform: Improve app performance and driver-matching by fine-tuning existing algorithms based on data points from millions of rides.
  • Cybersecurity: Develop machine learning algorithms to track and counteract common cyber attacks and payment fraud attempts.
  • General Research: Analyze bulk data from across the Uber app to better understand use cases, user retention (often called “churn”), and ride hotspots.
  • Marketing: Measure the effectiveness of marketing campaigns and develop predictive models to inform future campaigns.

They also have separate data science teams that work on similar challenges for UberEats.

Regardless of the team, Uber is looking for data scientists with an understanding of business analytics, modeling, machine learning, and deep learning algorithms.


Application Requirements

  • Bachelor’s Degree or higher in statistics, mathematics, economics, computer science or other related fields OR completion of an accredited coding bootcamp.

  • At least 3 years of experience in A/B testing, exploratory data analysis, machine learning algorithm development, and statistical analysis.

  • Proficiency in Python and SQL. Other language proficiencies may help your application, especially Java and R.

  • Experience with building pipelines and ETLs (Extract, Transform, Load) that intake huge datasets and produce actionable insights.


Data scientist interview structure

The interview process for Uber is a four-step process and takes a little over 1 week to complete.

  1. Initial screening: Resume-driven phone interview with a hiring manager.
  2. Technical screening: Phone interview that tests critical thinking and familiarity with ML algorithms
  3. Take-home assignment: A 3 section hands-on assessment due after 1 week.
  4. Onsite interview series: 5-6 interviews on SQL, machine learning, statistics, and behavioral/cultural fit.

Next, we’ll explore each of these steps in-depth and give you some tips to help prepare.


1. Initial screening

In the initial screening, a hiring manager or recruiter will call you. You’ll be asked to describe your technical experience and how they have shaped you as a developer. They’ll also discuss why you applied for the position and what you’re looking for in the role.

The interviewer will describe the role and team you’re applying for to see if you are a good fit. Next, the interviewer will go through your resume to hear more explanations.

Tips:

  • Highlight your personal contributions in past roles. “I worked on the design team” is less impressive than “I designed the interactive map and real-time location updates”.
  • Practice your personal elevator pitch beforehand. Be prepared to concisely explain what you did and what you learned for each item on your resume.
  • Practice explaining technical concepts for a beginner. Your interviewer may not have the depth of knowledge you do. Describe your accomplishments in approachable terms to demonstrate your communication skills.

Sample questions

  • What is a specific instance of how your predictive models influenced company decisions in a previous role?
  • Name a time when your model was incorrect and how you responded.
  • What makes you want to work at Uber?
  • How have your previous experiences prepared you to contribute to Uber?

The interviewer may also ask general data science questions to see if you have the knowledge you claim to have. For example:

  • What is marketing attribution?
  • What is P-value?
  • How can you measure the effectiveness of a machine learning model?

2. Technical screening

After the initial screening, you’ll get another phone call from an Uber data scientist for a 45-minute technical interview. Here you’ll be given Uber-related case studies and be asked to answer questions based on the data.

For example, you might be given a heap of all recorded data for one month of rides in New York City and be asked to explain how you’d select features that measure success.

Two staples of this interview are machine learning related questions, like feature selection and the tradeoffs of different ML algorithms, and specific analytics questions like anomaly detection.

Uber also prioritizes scalability, so you must be prepared to discuss strategies that create scalable solutions.

The technical screening primarily tests your problem solving and critical thinking skills for Uber-specific problems.

Tips

  • Prepare to answer questions related to the field you’re applying to. If you’re interviewing to be a data scientist for the marketing department, you can anticipate advertisement-based questions than if you were applying to work in platform optimization.
  • Describe any tradeoffs and design decisions you’re making to the interviewer. Thinking aloud shows the interviewer that you’re thinking critically about each part of a question and demonstrates your knowledge.
  • Don’t forget to review the fundamentals: Python data structures, classification vs. regression, recursion, dynamic programming, Big O, etc. Brush up on these to help you identify when they’d help optimize solutions.
  • Review Uber’s architectures, tech stack, and processes on their engineering blog. You can impress your interviewer if you can speak to how a solution will fit into Uber’s existing tools.

Sample questions

  • What is the difference between supervised and unsupervised learning? What are the strengths and weaknesses of each? Which would you use for the given case study?
  • Given a set of raw data, explain how you would clean the dataset to predict ride request density.
  • Given a marketing data set on all marketing channel costs and resulting signups/first trips, propose a short-term marketing plan that will maximize marketing efficiency.
  • What are key performance metrics for Uber? How would you rank their importance?

Keep prepping for your Uber interview.

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3. Take-home assignment

The next phase is a three-part take-home assignment with long-form questions on data science and programming skills. The assignment must be returned within a week and will feature a mix of code writing and written response questions.

Interviewers will expect longer and more deeply explained solutions for questions in this segment.

The three sections are: SQL and Analytics: You’re provided a real-life problem for Uber and an SQL schema. You’ll then write SQL to solve analytical problems related to the real-life problem.

  • General statistics: Contains a collection of short answer questions on metric evaluation and statistical experimentation.
  • Machine learning modeling: You’re provided a dataset and must create a predictive model to answer a product or business problem.

This step is a job-focused coding interview that tests for the specific skills needed in the role. The questions for this section change periodically, but they’ll always involve the above subjects.

You may also be expected to write up a report or present your take-home assignment in a PowerPoint during the next onsite stage.

Tips

  • Don’t forget to clean your data. The interviewers are interested in the entirety of your process, not just the answer.
  • Use tools and methods you’re most comfortable with. It’s better to answer the questions well rather than use tools you think the interviewer will be impressed with.
  • As with the technical screening, write down any tradeoffs and decisions you make when solving the question. This will give you impressive talking points for your presentation or report.
  • Use the full week. There is no extra credit for turning in the assignment early. If you finish early, use the rest of the time to double-check your answers or add more explanations.

Sample questions

  • What factors are most correlated with user churn? Propose a business solution to counteract the highest correlated factor.
  • Given a current machine learning algorithm, how would you optimize the model to provide more accurate results?
  • What machine learning model would most accurately predict which driver applicants are most likely to begin driving for Uber?
  • Provide a brief report that documents your assumptions, limitations, and suggestions for improving Uber’s driver acquisition.

4. Onsite interview series

The final stage of the process is an onsite interview day with five or six 45-minute interviews in succession. This stage is the most intensive of the interview process, but it also gives you the opportunity to stand out.

Usually, the interviews will consist of:

  • A behavioral interview with a product manager where you discuss past challenges and interpersonal problem-solving.
  • An interview with a hiring manager that covers a deep-dive on Uber’s company structure and describes how your team fits into that structure.
  • An interview with a product manager that covers core values and cultural fit (this is sometimes a subsection of the hiring manager interview)
  • An interview with a data scientist with open questions on business analytics, probability, and statistics.
  • An interview with a machine learning data analyst that focuses on modeling and ML design questions.
  • A coding interview in SQL or Python that tests data structures and algorithms. You’ll complete this either on a whiteboard or HackerRank.

Tips

  • Avoid using outside libraries. You can inform the interviewer that you know another library that could help, but you’re expected to solve problems with the standard library.
  • Scalability and availability are the most important considerations for Uber, so speak to how your solutions support these at every opportunity.
  • Use specific Machine Learning components to discuss long-term goals, improvements, and scaling.
  • Don’t forget to prepare for the behavioral interview by thinking about past conflict-resolution or leadership experiences. Many applicants overlook the 3 “non-technical” interviews but these are often the tie-breaker between you and equally matched applicants.
  • Ask insightful questions in the last 5-10 minutes of each interview. Uber and other top tech companies are interested in action-biased applicants. Questions demonstrate your engagement with the content and present you as a driven employee.

Sample questions

  • Define CLT. How is it relevant for Uber?
  • How does surge pricing work? What factors should we use to determine if surge pricing is necessary?
  • How would you find the lifetime value of a driver?
  • How do you draw a uniform random sample from a circle in polar coordinates?
  • How can we incentivize drivers to visit high traffic areas? Select and develop a model to track the effectiveness of this incentive.

General data science interview questions

  • Describe binary classification. What are its real-life applications?
  • Calculate the AUC of an ROC curve.
  • What is the purpose of sampling? What are some common types of sampling?
  • What is caching and why is it important in data science?
  • Explain linear regression, its assumptions, and its mathematical equations
  • What is logistic regression? How does it differ from linear regression?
  • What metrics would you use to record the success or failure of an advertising campaign?
  • What factors could increase the wait time to find a driver? How would you reduce user wait time?
  • How does your data analytics process change when working with big data?
  • 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).
  • What machine learning algorithms would best describe the likelihood of a driver accepting a ride? What assumptions are you making about the model?
  • List the pros and cons of deep learning. What area of Uber would best benefit from deep learning?

Wrapping up

As you’re preparing for your Uber interview, keep practicing both Python coding and high-level machine learning concepts. The best way to learn is to practice as many interview questions as you can, so don’t give up!

To help you prepare, Educative has created the Python Data Analysis and Visualization Path. This path lets you brush up on your coding and visualization skills in Python and popular Python tools like Panda, Matplotlib, and Numpy, all with hands-on practice.

By the end, you’ll be able to walk into any of the Uber interviews with confidence.

Happy learning!


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WRITTEN BYRyan Thelin

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