Monzo Bank Data Scientist Interview Questions
& Process

I interviewed for a Senior Data Scientist position at Monzo, which felt more like a data or product analyst job focused on experimentation (A/B testing), reporting, and talking to stakeholders. Machine learning and Python weren't mandatory but were a plus. The recruiter was a bit difficult, they cut me off when I mentioned past experience and insisted on examples from my most recent job, as if older work wasn't relevant. The second interview was better; I talked about experimentation even though it wasn't my primary duty. The third round involved coding and case-style experimentation questions. Some questions were so long the interviewer had to summarize them live, which seemed a bit much. You'd have to be in a very similar role already to do well; it's not something you can prep for quickly, even though the actual skills aren't that complex. They seem to want someone who's an almost exact fit for a specific analyst profile. If you have a modeling or academic background (like a PhD), your experience might not be valued. The whole process was frustrating.

The interview process kicks off with a chat with the data manager, focusing on your CV and past work. Next, there's a take-home assignment, but heads-up, it doesn't actually get reviewed, so don't stress too much about it, it's just a formality. Then, a case study interview happens, which was pretty weird and didn't feel relevant to a data scientist role. For instance, they asked about the price elasticity of demand curve, which is more economics. One case study was open-ended initially, but they steered us to ask specific questions to reach their expected conclusion, shutting down other ideas. It felt less like a data science assessment and more like a 'spreadsheet analyst' job.

The interview process started with a recruiter screening, mostly discussing your current role experience and why you're interested in Monzo. Then, a hiring manager interview focused on behavioral questions, asking about stakeholder management, research design, and past projects. Following that was a take-home test where you had to solve some basic SQL questions using two provided datasets. The tech screen involved optimizing the SQL query from the take-home test, followed by questions on A/B testing, including the technicalities of designing, running, and making launch decisions for tests. Examples of questions included identifying test types (T-test vs. Z-test), reducing sample size, defining sample size requirements, and hypothetical questions about efficient database storage. The final stage included a product sense interview with two questions: one about running an A/B test (metrics, launch decision, outcome strategies) and another about measuring share of wallet. There was also a behavioral interview covering stakeholder management, navigating complexity, prioritization, and leaving things better than you found them.