3 ms·
"we hadn’t really planned for so many good applicants so we were very slow to respond to everyone" So they weeded out the really bad applicants and as a result
by rheide 15y ago
"we hadn’t really planned for so many good applicants so we were very slow to respond to everyone"
So they weeded out the really bad applicants and as a result were left with so many good applicants that they're now in the same situation as the original article.
The key takeaway for me is not how people filter their applicants on real criteria, it's what they do when they have a large amount of seemingly equally skilled applicants. The original article demonstrated the 'unlucky' solution, I'm not sure what this article demonstrates since they still got too many applicants and they're spending time responding to all of them.
- SatvikBeri 15y agoHaving a lot of highly skilled applicants is a much better than having a lot of unskilled applicants. In the former situation, you pretty much can't go wrong...even if you use completely arbitrary filters, it doesn't really matter. This article demonstrates a way to get a lot of highly skilled applicants-that's extremely useful.
- yummyfajitas 15y agoSo they weeded out the really bad applicants and as a result were left with so many good applicants that they're now in the same situation as the original article. The original article talks about finding 4-5 people worth talking to out of 100 people, 50 of whom can't fizzbuz. We were in the situation of 20 interesting people out of 40 candidates, all of whom could fizzbuz. If it were truly unmanageable, we'd probably have used weak Bayesian filters. E.g., degree, rockstar ninja, etc, all the things Raganwald originally advocated against using. I gather this is what Google and Facebook do.