The discussion focused on practical data science applications, core ML concepts, project impact, challenges faced, and hands-on experience with generative AI, along with aptitude questions to evaluate logical and quantitative reasoning. Questions were designed to assess depth in machine learning algorithms, clarity in explaining past projects, exposure to real-world data problems, and basic understanding of generative AI models like LLMs or GANs. The interview involved detailed questions on end-to-end project workflows, feature engineering, model evaluation, and interpretation, alongside foundational ML theory, and simple but tricky aptitude puzzles to test analytical sharpness. Emphasis was placed on explaining the objective, approach, and outcome of data science projects, covering challenges faced, tools used, and learnings, followed by fundamental machine learning and aptitude-based reasoning questions. They asked about practical applications of ML, tools like Python and SQL, explanation of domain-specific projects, and theoretical questions on generative AI, including use cases and differences from traditional ML. In addition to project walkthroughs and technical stack used, questions explored knowledge of supervised and unsupervised learning, model tuning, error analysis, and real-world implications of AI solutions.