Predictive hiring tools are best suited for early-career candidates
Frida Polli · Matching People to Careers Bias-Free // Frida Polli, Pymetrics (FirstMark's Data Driven) · Feb 3, 2017 · at 22:54
Frida Polli, co-founder and CEO of Pymetrics, explains how her company's neuroscience-backed job candidate screening platform fits into different stages of an applicant's career.
0:00 / 0:45exact quote · 45.4s
▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark
“Yeah, so I think that this solution is honestly best suited for, let's say, the first 10, 15 years of this process. I don't think it definitely can work with experienced hires, but the reason I think it works better earlier on is a couple reasons. That's really when there's the lowest signal in your resume. I mean, if you think of, you know, what 21 year old people's resumes look like, is there really anything much on there that's of great help? Unless again, you're in a highly technical field where you've got some, you know, hard skills that you've developed, right? But other than that, the signal is very low, right? So what I would argue is that you, Experience can be brought in but I think that right now the problem that we're best suited to solve is really kind of like millennial hiring millennial hiring and under, you know.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Frida Polli
Neuroscience games lack inherent gender or ethnic bias
“Unlike some of the other data that people are using to predict fit in people, the games were generally, are generally not thought to have any inherent gender or ethnic bias, so they're not picking up on that if the training sample is biased.”
Resumes and questionnaires are subjective, low-density hiring tools
“Both of these are low density and also subjective.”
Candidate models require a broad baseline contrast group
“What we're saying is, no, you really need to collect a baseline that is representative of the people that you're, you know, trying to select from and use that as your contrast group.”
High-density objective data yields superior prediction of human outcomes
“And so what we just, what we learned in our, you know, 10 years at Harvard and MIT doing brain imaging is that if you collect better, more objective, more high density data on people, you can actually do a better job Of predicting research outcomes.”