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“We'd take the first non-idiotic person who applies.” I think it would still help for a company like that to display stats. For example, if you’re a qualified

“We'd take the first non-idiotic person who applies.” I think it would still help for a company like that to display stats. For example, if you’re a qualified candidate who is applying early, you’d see an abnormally high probability of callback. If you’re an unqualified candidate, you’d see a very low probability of callback. I mentioned that ML can help calculate these probabilities. In addition, I think it might help for companies to secretly encode “hard” job requirements. Most requirements are in fact soft, but the applicant never knows which is which. If they don’t meet a hard requirement, they should see a 0% probability of callback. Full stop. ML would do a good job of assigning importance weights to each of the requirements which aren’t encoded as “hard,” but it would start from weights provided by the employer when there isn’t enough data.

tom_b 7y ago
But stats like that also can signal being over-selective. I would gently suggest that it is a mistake to think in terms of unqualified vs highly-qualified candidates and ML for "callback probability". I don't want a good, but not FANG-level, candidate to NOT apply for our standard software engineer opening just because 74 people already did. Why? We had 60+ candidates for a recent posting for a junior developer role with some experience - think 1-2 years or some good school projects. Somewhere around 45-50 of those applicants were easily HARD-FLAGGED don't interview at all after a quick cover-letter and resume review by a technical person using a rubric. That rubric was scoped only to weed out applicants with absolutely no meaningful experience. While I also hate the laundry-list approach for job listings (Must Have: Expert Java and C#, Spring ORM and ActiveRecord skills with deep understanding of React internals), I think the best way to attack that is with a short, but focused cover letter. That should include a short paragraph or two about a candidate's hands-on experience and speculate how their experience might apply to the position. I guess what I am driving at is that this idea of ML assigning importance weights to "soft" vs "hard" requirements is, at best, just another low-value signal in job postings that are already full of low-value signal.
- keenmaster 7y ago
  Just like on your LinkedIn profile, there would be way more than a simple list of keywords and skills. The keywords would be part of a full profile which includes universities/degrees, certificates, years of experience, accomplishments, etc...some of which are easier for an ML to train on than others, but my point is those are stronger signals than “Java: Yes.” If there is residual fuzziness in the process, the platform can add skill tests (taken at a test center) that can get listed on your profile. A third party ecosystem of skill tests could emerge, some of which would be more prestigious/respectable than others. Some would have a binary output, and some would give you a score that is benchmarked against the population of test takers. This would only really be necessary for more technical skills. I’m not talking about rinky dinky khaki cubicle test centers. I’m talking about new age corporations with $1B market cap that develop an unparalleled ability to assess skills so that corporate HR departments don’t have to. These testing companies would be part of the ecosystem developed by the theoretical ideal, statistically-oriented, applicant friendly job platform that I’m talking about.
muzani 7y ago
Well, the thing is that interview calls are based almost entirely on resume and cover letter quality, not so much the skills. A hard requirement is that they should understand how case-sensitive and punctuation works, and many applicants can't even get their name in proper Name Case. Feedback is, well, if you school for 11 years and still can't use grammar where it matters, I'm not sure it helps. But too many do this. We did reject one smart guy for an Angular post, because his only technical skill was WordPress and the gap for that is just too big. But we interviewed people with experience with only PHP and no JS. Skills are learnable. There's also weird stuff like rejecting people because their expected salary is too low. It's a pattern that those with low salary expectations performed worse technically than juniors. 0% callback is also rare because of that. I got my first, web dev job, with no experience in anything other than HTML and an EE degree. Rejection rates for a role are 100% until the first hire.

tom_b 7y ago

But stats like that also can signal being over-selective. I would gently suggest that it is a mistake to think in terms of unqualified vs highly-qualified candidates and ML for "callback probability". I don't want a good, but not FANG-level, candidate to NOT apply for our standard software engineer opening just because 74 people already did. Why? We had 60+ candidates for a recent posting for a junior developer role with some experience - think 1-2 years or some good school projects. Somewhere around 45-50 of those applicants were easily HARD-FLAGGED don't interview at all after a quick cover-letter and resume review by a technical person using a rubric. That rubric was scoped only to weed out applicants with absolutely no meaningful experience. While I also hate the laundry-list approach for job listings (Must Have: Expert Java and C#, Spring ORM and ActiveRecord skills with deep understanding of React internals), I think the best way to attack that is with a short, but focused cover letter. That should include a short paragraph or two about a candidate's hands-on experience and speculate how their experience might apply to the position. I guess what I am driving at is that this idea of ML assigning importance weights to "soft" vs "hard" requirements is, at best, just another low-value signal in job postings that are already full of low-value signal.

keenmaster 7y ago
Just like on your LinkedIn profile, there would be way more than a simple list of keywords and skills. The keywords would be part of a full profile which includes universities/degrees, certificates, years of experience, accomplishments, etc...some of which are easier for an ML to train on than others, but my point is those are stronger signals than “Java: Yes.” If there is residual fuzziness in the process, the platform can add skill tests (taken at a test center) that can get listed on your profile. A third party ecosystem of skill tests could emerge, some of which would be more prestigious/respectable than others. Some would have a binary output, and some would give you a score that is benchmarked against the population of test takers. This would only really be necessary for more technical skills. I’m not talking about rinky dinky khaki cubicle test centers. I’m talking about new age corporations with $1B market cap that develop an unparalleled ability to assess skills so that corporate HR departments don’t have to. These testing companies would be part of the ecosystem developed by the theoretical ideal, statistically-oriented, applicant friendly job platform that I’m talking about.

muzani 7y ago

Well, the thing is that interview calls are based almost entirely on resume and cover letter quality, not so much the skills. A hard requirement is that they should understand how case-sensitive and punctuation works, and many applicants can't even get their name in proper Name Case. Feedback is, well, if you school for 11 years and still can't use grammar where it matters, I'm not sure it helps. But too many do this. We did reject one smart guy for an Angular post, because his only technical skill was WordPress and the gap for that is just too big. But we interviewed people with experience with only PHP and no JS. Skills are learnable. There's also weird stuff like rejecting people because their expected salary is too low. It's a pattern that those with low salary expectations performed worse technically than juniors. 0% callback is also rare because of that. I got my first, web dev job, with no experience in anything other than HTML and an EE degree. Rejection rates for a role are 100% until the first hire.