In the first round with Teresa, the discussion began with behavioral questions, such as how I handle disagreements, conflicts, and feedback, as well as a time when I had to learn new tools like SQL. I introduced my project and answered specific questions about classification algorithms, overfitting prevention, and cross-validation. While I mentioned techniques like penalizing variables and removing outliers, I realized I could have elaborated further on cross-validation. We also discussed my Kaggle competition experience, where my data visualization skills seemed to make a strong impression. Teresa explained the role and team dynamics, and I asked about pain points in the role, which she appreciated. The interview concluded with her informing me about a take-home assignment. In the second round, the focus shifted to technical questions, including SQL queries (live coding) and data science topics. For a dataset with many columns and few rows, I explained how to address dimensionality issues and walked through the approach step-by-step. I discussed decision trees, their important features, and ensemble methods like random forest classifiers. Questions about supervised vs. unsupervised algorithms allowed me to dive into preprocessing needs and share my understanding. There was an emphasis on communication skills and how I translate technical concepts to non-technical clients, which I demonstrated with examples from my experience. I asked thoughtful questions about challenges and pain points, which led to meaningful discussions.