Experian Data Scientist Interview Questions
& Process

The interview process had around 2-3 rounds. It started with the Hiring Manager, where they asked some basic questions about your resume and RAG-based questions. So, be prepared for that. Then there was a technical round, and a final round.

The first step involves submitting an application form and completing a numerical test. Following that, there's a video interview. The final stage is an assessment centre, which includes a group exercise and an interview with senior team members where they'll ask competency and behavioural questions.

The first step was a quick 30-minute phone chat, mostly just going over my background. After that, I had to go to their office to give a presentation on a recent NIPS paper I worked on. This was followed by technical interviews. These technical interviews were split into two main parts: one focused on programming and the other on machine learning and statistics. The ML/stats part was pretty in-depth, covering topics like eigenvalues, matrix factorization, MLE, dimensionality reduction, correlation, parameter estimation, confidence intervals, and kernel density estimation. It wasn't a walk in the park, but the questions seemed reasonable. The programming interview, however, was a bit of a letdown. The interviewers didn't seem to have a strong CS background and focused heavily on niche Python details like iterators, generators, inheritance, and multithreading. It felt like questions you could look up in minutes, not really testing core programming concepts. They seemed more interested in how to override Python's hash function than a deep understanding of hashing itself.