The interview consisted of multiple rounds covering both technical and practical aspects of data science work. They asked about my previous work experience, tools I used, data processing scale, coding practices, and MLOps skills. The session included classic machine learning questions about when to use traditional ML versus deep neural networks, and detailed questions about ensemble methods, boosting, bagging, XGBoost, random forests, and decision trees. They also inquired about challenges I faced in previous projects and how I resolved them. After interview I got my interview feedback in the next day.

There were three stages - one screen HR call, then one technical interview, then one management call. The HR call was about the expectations - both about the company and salary. The technical interview was all about past data science experience (+ some additional theoretical questions, but they weren't hard).