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:
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:
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.
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.
The interview process for Uber is a four-step process and takes a little over 1 week to complete.
Next, we’ll explore each of these steps in-depth and give you some tips to help prepare.
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.
- 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.
The interviewer may also ask general data science questions to see if you have the knowledge you claim to have. For example:
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.
- 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.
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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.
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.
- 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.
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:
- 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.
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.
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