The interview process was quite structured and technical. It began with basic discussions about my experience, followed by a detailed round focusing on web scraping techniques — how to handle dynamic pages, avoid blocking, and manage large-scale data extraction efficiently. Later, the interviewer moved to classical machine learning algorithms, asking about concepts like decision trees, random forests, logistic regression, and their practical use cases. They also wanted to know how I would select features and evaluate model performance. Overall, the process tested both my applied programming skills and theoretical understanding of ML fundamentals.