2 Rounds: Round 1: Technical What is the difference between bagging and boosting? Explain the bias–variance tradeoff. How do you handle an imbalanced dataset? Which metrics do you use for regression and classification problems? What is ROC–AUC and when should it be used? Explain the process of deploying a model to production. What steps would you take if a model’s performance drops after a few months in production? What is hypothesis testing and what does a p-value represent? How do you handle datasets with a large number of missing values? L1 L2 Regularisation Round 2: Managerial What is the difference between machine learning and deep learning? How do you handle conflicts between team members? Explain one project end-to-end, from requirement gathering to deployment. What would you do if a model fails during rollout? How do you monitor a deployed model? How do you manage junior team members? How do you manage communication with clients and senior management?