Had several rounds with people from various functions, and overall the conversations were thoughtful and engaging. Most of the earlier interviews focused on conceptual thinking, modeling approaches, and communication/problem-solving style. The final stage involved a take-home exercise and presentation, which was worthwhile and relevant to the role. During the presentation, interviewers added comments and questions directly into the document in real time, which made parts of the discussion slightly more difficult to navigate. There was also a strong emphasis on live Python coding and algorithm-style questions, so candidates should be prepared to demonstrate coding fluency under time pressure in addition to conceptual data science knowledge. Overall, the process was organized and communication was responsive throughout. However, I found parts of a post-interview feedback conversation relatively brief and quite direct in tone, and it did not provide much specificity around the evaluation or how the decision was made. While I appreciated the opportunity to discuss it, I would have valued more concrete feedback on areas of mismatch between the role requirements and my performance.