[tom_b]

Matches my experience on the hiring side.

In the group between "don't qualify" and "can't afford", we have a hard time with candidates who are unable (or maybe lack the communication skills) to effectively talk about how their experience+potential are a good match for even a generic job opening.

We don't do any algorithmic/challenge screening during interviews.

A common interview fail for us is to have a candidate that can't generalize from or build on something from his/her resume. This usually comes out during "tell us about project X that you worked on" and comments from our side like "oh, that sounds like project Y here. But Y is different this way - what are your thoughts about how to handle <insert small-scoped, specific difference>?"

[keenmaster]

Stats would be displayed prior to getting any interviews. If someone gets an interview and then fails at it, that’s on them. Stats can still help with baseline interview performance though. Machine learning algorithms can find a correlation between experience (or lack thereof) and interview failure. In response, the algos will encourage those candidates to apply somewhere where they have a better chance of getting in. However, those candidates can supplement their skill set. They can take tangible steps to increase their likelihood of getting the job.

Let’s say the applicant is a senior engineer but you’re afraid they aren’t keeping up with the latest frameworks. And let’s say that the employer (or the ML also) assigned high importance weights to those frameworks. Here’s what you’d see happen:

- The senior engineer with 15 years of experience at several BigCos but doesn’t have experience in that framework will see a low probability of callback

- The senior engineer with 10 years of experience and only one BigCo who’s skilled with that framework (the right keywords, and 2 MIT edX certificates in it) will see a high probability of callback.

What’s exciting is that this will encourage people to acquire exactly the skills that will help them get the job that they want. The applicants would be able to do scenario analysis on the job platform and see how much better their chances would be if they acquire a certain skill set. This would allow them to tune out the noise and focus on what it takes to get the type of job they want. Of course, the more senior the applicant is, the more accomplishments at past jobs matter. Even then though, senior applicants usually only display a fraction of their past experience. The platform can show their callback probability change in real time if they add a meaningful past experience or accomplishment.

The possibilities are endless. Are you considering doing a XY boot camp because you think it might get you into Z Co? Think again, with your experience + that boot camp, you will have a 2% chance of getting into Z Co and 5% chance of getting a similar role with similar pay. That alone would improve the educational and vocational pipeline.

[tom_b]

The response is enthusiastic, but kind of misses the point my original comment was making. All I was saying in that comment is that as a hiring organization, we bring in candidates who on paper have the skills and experience we are looking for, but flame out in spectacular fashion when asked to talk about their skills, experience, and how they might apply to our specific problems and situation.

Note that I am carefully NOT saying, oh the candidate knows javascript, but not vue.js, so they don't fit for us. I am also not saying "oh, if only candidates knew we wanted them to get vue.js certified and it would be more likely we would put them in our interview loop."

I don't think the problem of how candidates can better understand why they are or are not getting callbacks from employers maps very well to any stats-based measurement of job posting descriptions against applicant resumes/CV/LinkedIn profiles.

My only thoughts on the hiring process are really anecdotal, but I believe a few things to be true.

  1 - Most job descriptions are crap.
  2 - Most jobs for software engineers can be done by any
  reasonably trained and/or experienced candidate 
  who can pass a work-sample test.
  3 - Totally untrained and inexperienced candidates 
  seem to apply to lots of jobs.
  4 - Software engineer hiring should be ideally 
  based on work-sample tests.
  4a - I have a *very* hard time finding companies 
  that do #4.