Metromile Data Scientist Interview Questions
& Process

I applied through a referral and heard back from a Technical Recruiter within 4-5 days to set up an initial hr screening for the following week. The recruiter started by talking about the company and the role, then asked me basic questions about my resume, salary expectations, and where I'd like to work. They also explained what would happen next. After that, I had another screening with a DS Manager. This part had two sections: first, I had to talk about my past work in DS, and second, there was a coding challenge on Coderpad. The coding part involved a question about product sense and experiment design for A/B testing, followed by two SQL questions (one easy, one medium). Finally, I had a Python coding test where I needed to implement a basic ML algorithm from scratch using the provided data, without any libraries. The hiring manager then answered some of my questions. It was a good experience overall, though I didn't get the job because I wasn't well-prepared for the test.

The hiring manager was really accommodating when I mentioned I had another offer and needed things sped up, answering all my questions. The first round was a review of my previous projects. The second round also covered my past projects, with questions about general statistics and A/B testing. Then came coding challenges: two SQL and two Python questions, with the second question for each being tougher. That second interview was an hour long. Although I was selected to move on, I had to decline due to my offer's timeline. The final round would have been a 3-hour session involving a case study and coding a model, which they send you the description for beforehand. I definitely recommend interviewing here; the questions are practical and test your real job understanding, not just brain teasers. Metromile truly showed kindness and empathy during the process.

First, there was a recruiter call to talk about the job. Then, I had a call with a data scientist who looked over my resume and asked standard data science questions about concepts like overfitting and cross-validation. The onsite interviews involved talking with data scientists, including coding tasks, a cross-functional interview, and a leadership interview. It wasn't as technical as I expected. The company seemed a bit disorganized and unsure about this position.