Will A Machine Or Human
Help You Find Your Best Job?
As someone who is always fascinated by the possibilities of machine learning, I ask myself how computer intelligence will seep into the recruitment world and in what form.
In today’s current climate, there’s no doubt in my mind that a human recruiter can deliver a lot of insight on the company culture for a candidate’s benefit. A machine can’t fully comprehend certain elements that make for an outstanding fit like culture. Yet.
Still, will there ever come a time when computers can deliver an optimal match of candidates and cultures? Yes, I believe so. But we’re still a long way away from that. To truly have a superb cultural match via computing, those computers need so much more input. As much as we think that machines know everything about us (think how much certain people distrust Facebook), there’s a lot more data for computers to obtain.
Even if Facebook were to know 1000 things about you, the human brain has 100 billion neurons and more than 700 trillion synaptic connections — in other words, that’s one incredibly complex structure with innumerable patterns per person that computers haven’t entirely figured out.
What we do know is that machine learning is growing, not subsiding. Machines like IBM’s Watson are not exactly a passing fad. As machine learning grows and the quality (and quantity) of data about ourselves grows with it, we will have systems that are much better at matches on many levels.
What gives me reason to believe this? Look at where other industries are going, like the medical industry, for example. Sure, a doctor may want to know certain thing in relation to your blood pressure, your pulse, your cholesterol and more. But how many data points are we really talking about? Even the most robust exams are sharing 50 points of data or less. What about if we had 3000 data points of information on ourselves? What about 10,000 data points — all collected in real time? Machine learning may be able to collect that kind of data in time. And if we can get that level of detail medically, why can’t a machine perform a similar multi-factor snapshot of various organizations and arrive at a better match of potential employee and company? That’s not far-fetched at all.
In fact, one startup affiliated with MIT is giving us a tiny glimpse of this future. Recently, the startup had a few subjects play a video game for 20 minutes — as it turns out, you can learn a great deal about a person by the way they play video games. One person could pick a more aggressive strategy while another is far more passive. One could play incredibly fast while another is slow and methodical. We can even obtain insights on how creative a person is based on how they play such games. All of which and then some is used to get a better sense of how the person fits into the company’s environment.
Of course, understanding what fully constitutes the culture starts with us humans, especially if we ever have any intention of having computers help us find a cultural match. In the best situations, if we speak to anyone from the management to the lower employees, we should be able to see an astute model of what culture means to them. What kind of people do we hire? What goals do we aspire to? What makes us tick as a company? What kind of people succeed here? Like so many industries impacted by machine learning, we still need the presence of a human “helper” so the machine can decipher these questions with greater accuracy.
When you have a good recruiter that has a base of knowledge from quantity and quality of interactions over many years of doing business, you’re starting the process from an advantage. Even before machines are involved. They’ll have an expectation of where the person should be most effective within the organization. They’ll have an informative point of view beyond just sending a resume to a company that might have 100 openings (and essentially saying “Here’s my talent, you figure out where I should go,” which is never optimal).
Contrast this with a recruiter who only looks at the open job and doesn’t have a relationship with a variety of companies already in place. They don’t know the environment. They only know what’s right in front of them as far as a candidate who seems to fit the role. They can think about “what is” but they can’t comprehend “what could be” in terms of the complete landscape of the company culture. Consequently, a candidate may be positioned too narrowly and miss out on other opportunities.
In other words, when a recruiter who is an expert in select industries and has existing deep company relationships combines this background with machine learning’s advancements, the candidate will be in an ideal position to find the best company culture for their career.
A partnership of machines and humans in this way isn’t just exciting. It’s more likely than we expect — and a more positive outcome than many of the other predictions of the future we read about almost daily.
Ilya Talman is the Founder and President of Roy Talman & Associates, one of the top executive search firms connecting exceptional talent to the world’s finest financial trading firms and institutions. Headquartered in Chicago, RTA has been recognized for over 30 years for its deep financial industry knowledge and a process that goes beyond the resume to factor in chemistry as much as credentials. Call us at 312.425.1300 or email info@roytalman.com.
