Branch International

I appeared for the online assessment and, after clearing it, was shortlisted for the interview rounds. The technical interview was a mix of DSA, statistics, machine learning fundamentals, and project discussion. The coding question was a dynamic programming problem involving finding the minimum cost/path while dealing with obstacles. The interviewer was more interested in my approach, state definition, and edge cases than just the final code. After the coding question, the discussion shifted to statistics. I was asked about common statistical tests, including the Z-test, T-test, ANOVA, and Chi-square test. The interviewer wanted to know when each test should be used and the intuition behind them rather than just their definitions. There were also a few machine learning questions, including explaining the difference between Precision, Recall, and F1-score, and in what scenarios each metric is more appropriate. The remaining part of the interview was a deep dive into my projects. The interviewer picked one project from my resume and asked detailed questions about the overall pipeline, why I chose a particular model, the preprocessing steps, evaluation metrics, challenges I faced, and what I would improve if I had to build the project again. There were several follow-up "why" questions, so it definitely helped to know every design decision instead of just remembering the implementation.