The technical rounds and the case study were focused on traditional ML. How would you deal with columns containing hundreds of categories? How would you deal with class imbalance? How does xgboost deal with nan values? What is the difference between oversampling and class weights? What hyper-parameters did you use in your models? How did you decide between one hot encoding and target encoding? What was the loss function used and what the score function used and why they were chosen? Then there were questions regarding one of the projects you did and questions on that? Behavioural round - How would you deal with a low performer in your team? What challenges you have faced? What do you consider as failure? Hypothetical scenarios on linking your models to business KPIs ? How would you manage a project?
