I agree with what most people have said, but it will definitely vary from company to company and even between interviewers.
My personal preference for interviewing candidates has mainly been about someone's thought process around tackling a problem they aren't familiar with. There's such a broad array of people applying for roles that 9 times out of 10 you'll find people can't explain what the feature creation or feature selection process looks like - because in most courses and play datasets these things are done for you.
Many people don't even think about the implications of their answers. You'd be shocked at the number of people who say they would do a grid search to find the optimal parameters on a dataset with millions of rows.
I'd also say in many interviews now the company is setting you up to fail rather than trying to navigate the interview with you to show how you might be useful to the organisation. I would use that to see if the company are a good fit. If a company has 6 interview stages, they don't know what they want. If a company has 1 interview stage they probably aren't being rigorous enough (find out personal fit and technical fit). If a company makes you solve random leet code problems, or explain the architecture of an RNN, when in reality all they do day to day is use scikit-learn, is that interview really fit for purpose?
I agree with what most people have said, but it will definitely vary from company to company and even between interviewers.
My personal preference for interviewing candidates has mainly been about someone's thought process around tackling a problem they aren't familiar with. There's such a broad array of people applying for roles that 9 times out of 10 you'll find people can't explain what the feature creation or feature selection process looks like - because in most courses and play datasets these things are done for you.
Many people don't even think about the implications of their answers. You'd be shocked at the number of people who say they would do a grid search to find the optimal parameters on a dataset with millions of rows.
I'd also say in many interviews now the company is setting you up to fail rather than trying to navigate the interview with you to show how you might be useful to the organisation. I would use that to see if the company are a good fit. If a company has 6 interview stages, they don't know what they want. If a company has 1 interview stage they probably aren't being rigorous enough (find out personal fit and technical fit). If a company makes you solve random leet code problems, or explain the architecture of an RNN, when in reality all they do day to day is use scikit-learn, is that interview really fit for purpose?