Uber is a multi-national ride-hailing company with massive operations in over 785 cities worldwide. Their services range from ride-hailing and food delivery to logistics and micro-mobility.
Uber aims to:
Bring the future closer to its customers with self-driving technology and urban air transport, helping people order food quickly and affordably, removing barriers to healthcare, creating new freight-booking solutions, and helping companies provide a seamless employee travel experience.
To achieve this feat, Uber has slowly been incorporating data science and analytics in almost every department and service — such as risk management, marketing, and policy implementation.
The role of a data scientist at Uber varies across specific teams. Your role as a data scientist will be heavily determined by the team you are applying for. The data science role generally covers basic business analytics, modeling, machine learning, and deep learning implementation. Uber is a large company that has data science teams working in safety and insurance, rides, risk, platform, marketing science, policy, and Uber Eats.
Required Skills
The requirements for each job depends on the department. However, Uber generally prefers to hire qualified candidates with at least three years of experience unless for an associate position. The basic requirements for hiring are:
There are a large variety of data science teams at Uber that work spread out across different company divisions. The title “data scientist” at Uber falls under these two teams — Data Science and Data Science & Analytics.
Depending on the teams, their functions may include:
The interview process starts with an initial screen with a recruiter or hiring manager that lasts for 15 and 30 minutes. This is followed by an Uber take-home challenge. The take-home assignment covers SQL, experimental/business intelligence, and data analysis questions. Then a 45-minute technical phone screening follows the take-home challenge. After the technical screen is the on-site interview panel of five different interviewers.
After your application submission, this is a phone call interview with a hiring manager or recruiter. This interview assesses the job role, the team, and your general background with a potential light technical interview. Expect questions about your experience and how it could apply to Uber.
The hiring manager may also ask more high-level data science interview questions such as:
The hiring manager is generally looking for red flags. Make sure to review general modeling and analytics concepts and practice communicating technical concepts and projects.
Practice a SQL interview question:
Given a employees
and departments
table, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.
employees
table
Columns | Type |
---|---|
id |
INTEGER |
first_name |
VARCHAR |
last_name |
VARCHAR |
salary |
INTEGER |
department_id |
INTEGER |
departments
table
Columns | Type |
---|---|
id |
INTEGER |
name |
VARCHAR |
After completing the initial phone screening, you will receive a take-home challenge that you will have one week to complete. The take-home assignment comprises three sections:
Note that this take-home challenge has been generally standardized by Uber. Depending on the team, however, they may add changes to the original take-home challenge specific to the team.
The next step is the technical interview phone interview with a data scientist. Most of the time, the questions asked in this interview are Uber-related data science case studies looking for an open-ended response. The goal here is to test your critical thinking and problem-solving ability. Expect to receive machine learning problems like feature selection and model building, focusing on real-life Uber problems. If the role is more analytics-focused, you can also expect a product-based question.
The next step after passing the technical screen is the on-site interview. This is a full-day interview involving whiteboard coding, project discussion with team managers and data scientists, business case studies, and statistical concept discussions. The on-site interview consists of 5 or 6 rounds of 45-minutes each.
The panel generally looks like this:
Remember that the ultimate goal is to assess how you can apply data science concepts to Uber-related specific business problems. Brush up on knowledge of statistics and probability, A/B testing and experimental design, and modeling concepts.
Average Base Salary
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