I recently interviewed for the Senior Data Scientist position at this company, and the process involved three rounds. The initial round was an initial screening with the HR team, focusing on my current situation, CV, and reasons for seeking a change. Notably, emphasis was placed on prior experience in areas the company is actively developing, such as pricing or recommendation. The second phase comprised a technical interview with a Data Scientist and a Team Lead. During this stage, I discussed past projects, covering analytical aspects to model deployment. Questions delved into the rationale behind technique choices and problem-solving approaches, leading to interesting discussions on the interviewer's preparation level and confidence in third-party arguments, where it can be felt that they are not fully prepared for some topics like forecasting. Then the final part involved practical case involving the implementation of a pricing model was presented, requiring a solution or, at the very least, a brainstorming session within a few minutes. I observed a certain rigidity in expecting alignment with the company's internal mindset, which could be an area for improvement. It's crucial to note that lacking context about the company's domain at times made formulating robust answers in a limited timeframe challenging. For prospective candidates, I recommend acquainting themselves with the world of second-hand car sales, as it appeared relevant. Additionally, there were basic ML questions, such as differentiating between boosting and bagging, and inquiries about handling categorical variables. In summary, the experience wasn´t the best where lack of knowledge and ambiguous questions arose.