I would applaud the author on trying to take a data-driven approach to discover the best hiring signals, but I must offer one major critique of the approach: These data are ultimately not too meaningful unless you correlate actual job performance with interview performance. By the author's own admission, interviews are often faulty. By correlating final-round interview performance with early-round interview performance, you are really only predicting how candidates will perform in the final-round interview, which is not necessarily a good predictor of long-term job performance.
Which seems totally at odds with what their clients want and furthers the perception that tech interviewing has become a hazing ritual totally detached from evaluating the ability of the interviewee to actually do the job.
If you are serious about this then you should be interviewing candidates, deciding on whether you think they should get a job. Then giving them a job anyway and evaluating their performance after some time interval and see how accurate your interview process was in terms of weeding out those who would fail.
Absolutely, we keep in touch with companies post hiring and are gathering that data too. It won't be meaningful for a while though, interview performance gives us a tangible starting point.
In fairness, the author says at the end of the post that they do intend to rerun the analysis with actual job performance.
It seems reasonable to optimize for hiring right now since otherwise nobody would go to them to find a job, and it'd also be impossible to measure job performance when the people you collect data can not get jobs.
