Technical questions regarding classical data science and genai, such as how to evaluate models with umbalanced datasets. How to evaluate an llm based model. Don't know what would have been the other rounds

I interviewed for a data scientist role at Hiscox insurance, after the introductory stage with the recruiter I had a technical interview that lasted for 45 mins to 1hr : This involved technical questions around data science like define type 1 error, type 11 error, where to use them, explain decision tree to a non technicals audience, explain gradient boosting, A member then look through my resume and asked random technical questions based on my resume. Next stage was coding interview: I discussed a ml notebook with the panel . Simple questions like .info, common exploratory data analysis functions , model evaluation metrics , final stage was a behavioural interview that asked different behavioural questions like how do you like explain technical terms to non technical audience etc