The interview process was well-structured and balanced between technical and practical problem-solving. It started with an initial screening call where we discussed my background, key projects, and general understanding of data science concepts such as statistics, Python, and SQL. The next round involved a technical assessment focused on exploratory data analysis, SQL queries, and basic machine learning — mainly regression, classification, and feature selection. The questions were straightforward and tested practical knowledge rather than deep theory. In the final round, I met with team members to discuss real-world use cases and how I would approach data problems in their business context. They also assessed communication skills, teamwork, and how I present data insights clearly to non-technical stakeholders. Overall, the interview was conversational, fair, and a good mix of coding, analytics, and applied thinking.