Real, anonymous reports from people who interviewed for Data Scientist at Uber Drivers, newest first and distilled into what to expect across the loop.
My early step was a timed test with 17 questions in 60 minutes. It broke down into 2 medium SQL questions and 15 aptitude questions, and the aptitude portion felt noticeably easier—almost like I could knock out the majority in under half the time.
My first round centered on risk, and it started with a single SQL task that I completed. After that, they asked me about a confusion matrix, which I answered as well.
The interview process typically starts with a technical screening that includes SQL questions, often focusing on window functions and data manipulation, followed by a longer online assessment that tests SQL, statistics, and modeling skills. Candidates should be prepared for a mix of coding tasks and analytical questions that assess their technical depth.
Candidates can expect to engage in case study rounds that require them to analyze product metrics and apply their technical skills to real-world scenarios, such as A/B testing and fraud detection. It's important to articulate reasoning clearly and connect data analysis to business impact.
Behavioral interviews are a significant part of the process, focusing on teamwork, problem-solving, and cultural fit within the company. Candidates should prepare to discuss past experiences and how they align with the company's values and mission.
The interview process is structured into multiple rounds, often including a mix of technical, case study, and behavioral interviews, which can be mentally demanding. Candidates should be ready to switch between different types of questions and maintain focus throughout the process.
A strong emphasis is placed on experimentation and analytical reasoning, particularly in understanding concepts like network effects and causal inference. Candidates should be prepared to discuss experimental design and its implications on product decisions.
Candidates often reflect on the high expectations for both technical and analytical skills, with some noting that performance in one area can influence overall outcomes. It's crucial to prepare broadly and understand that the evaluation criteria may extend beyond technical correctness.