Real, anonymous reports from people who interviewed for Data Scientist at Robinhood, newest first and distilled into what to expect across the loop.
A former colleague pointed me to the Data Scientist role at Robinhood, and I ended up going through an initial recruiter call followed by a technical screen. The SQL questions hit quickly and felt trading-analytics heavy: I worked through user trading volume using SQL, and I also got an A/B testing design prompt where they wanted me to lay out how I’d structure the experiment and interpret results.
I got far enough to clear an initial technical phone screen, but the process quickly soured afterward. The recruiter asked about my on-site availability, and then I never heard back despite my follow-ups. It felt less like a scheduling gap and more like I’d been dropped from the pipeline.
Candidates typically start with a time-limited take-home challenge focused on data extraction and analysis, which can be time-consuming and may include SQL components. Feedback on this stage is often minimal, leading to frustration.
The technical phone screen often includes SQL and coding questions, with a focus on analytics and A/B testing scenarios. Candidates report varying experiences with interviewer clarity and engagement, impacting the overall assessment quality.
Interviews frequently emphasize analytics workflows, including model selection and interpretation of results from A/B tests, requiring candidates to demonstrate a thorough understanding of the processes involved.
Candidates report significant issues with communication throughout the process, including delays in feedback and lack of clarity regarding next steps, which can lead to frustration and a sense of being left in the dark.
The interview process is described as intense and demanding, with candidates feeling the pressure to perform under high standards. Preparation is crucial to navigate the challenging questions effectively.
Candidates have noted inconsistencies in how their technical skills were evaluated, with some interviews feeling mechanical or overly standardized, which may not accurately reflect their capabilities.