Eh, if I'm reading between the lines correctly, this experience is an artifact of their business model. It's a ludicrous hiring process in part because they need to claim to hire the top-3% for marketing purposes, which means they need to reject a ton of people, which means they need to come up with some way to screen out a lot of applicants quickly. They found one.

It's also a consultancy, so they really, really care about speed. They don't care about coding quality, getting rid of pandas, etc. They care about producing something they can bill for as quickly as possible, so you can move on and produce something else they can bill for as quickly as possible.

Re: top-3%. Joel Spolsky's article on this kind of bullshit metric is great. Short version: a ton of companies can credibly claim to hire the top-3% of applicants, because people apply to a lot of jobs, and because the worst people apply for a whole lot of jobs. The same people are in the denominator for all of the companies hiring the top-N%.

Are they hiring the top-3% of people? Of course not. There's no objective metric for that, and we know where the top-3% of data scientists work (roughly speaking) and it's not company X. Company X is just rejecting a large number of people relative to the number they hire. Universities do this too. They'll deliberately encourage huge numbers of people to apply that they know will be rejected solely to push down their acceptance rate and make them appear to be more competitive, which they hope will convince people that they're high-quality. Complete red-herring though. The quality of the people hired isn't related to N hired / N applied. It's about the applicant pool and the selection process. But it's a nice tricky metric for a consulting firm to throw around.