Using Machine Learning Tools To Find Employees Who Can Scale With Your Business

By Kimberly Cook |Email | Dec 4, 2018 | 4692 Views

  • Artificial Intelligence (AI) and machine learning are proving adept at discovering candidates' innate capabilities to unlearn, learn and reinvent themselves throughout their careers.
  • Applicant Tracking Systems (ATS) don't capture applicants' drive and intensity to unlearn and learn or their innate capabilities for growth.

Hiring managers in search of qualified job candidates who can scale with and contribute to their growing businesses are facing a crisis today. They're not finding the right or in many cases, any candidates at all using resumes alone, Applicant Tracking Systems (ATS) or online job recruitment sites designed for employers' convenience first and candidates last. These outmoded approaches to recruiting aren't designed to find those candidates with the strongest capabilities. Add to this dynamic the fact that machine learning is making resumes obsolete by enabling employers to find candidates with precisely the right balance of capabilities needed and its unbiased data-driven approach selecting candidates works. Resumes, job recruitment sites and ATS platforms force hiring managers to bet on the probability they make a great hire instead of being completely certain they are by basing their decisions on solid data.

Playing The Probability Hiring Game Versus Making Data-Driven Decisions
Many hiring managers and HR recruiters are playing the probability hiring game. It's betting that the new hire chosen using imprecise methods will work out. And like any bet, it gets expensive quickly when a wrong choice is made. There's a 30% chance the new hire will make it through one year, and if they don't, it will cost at least 1.5 times their salary to replace them. When the median salary for a cloud computing professional is $146,350, and it takes the best case 46 days to find them, the cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. The average size of an engineering team is ten people so only three will remain in 12 months. These are the high costs of playing the probability hiring game, fueled by unconscious and conscious biases and systems that game recruiters into believing they are making progress when they're automating mediocre or worse decisions. Hiring managers will have better luck betting in Las Vegas or playing Powerball than hiring the best possible candidate if they rely on systems that only deliver a marginal probability of success at best.


A Data-Driven Approach To Finding Employees Who Can Scale
Personalization at scale is more than just a recruiting strategy; it's a talent management strategy intended to flex across the longevity of every employees' tenure. Attaining personalization at scale is essential if any growing business is going to succeed in attracting, acquiring and growing talent that can support their growth goals and strategies. Eightfold's approach makes it possible to scale personalized responses to specific candidates in a company's candidate community while defining the ideal candidate for each open position. Personalization at scale has succeeded in helping companies find the right person to the right role at the right time and, for the first time, personalize every phase of recruitment, retention and talent management at scale.


Lessons Learned at PARC
Russell Williams, former Vice President of Human Resources at PARC, says the best strategy he has found is to define the ideal attributes of high performers and look to match those profiles with potential candidates. "We're finding that there are many more attributes that define a successful employee in our most in-demand positions including data scientist that are evident from just reviewing a resume and with AI, I want to do it at scale," Russell said. Ashutosh Garg, Eightfold founder, added: "that's one of the greatest paradoxes that HR departments face, which is the need to know the contextual intelligence of a given candidate far beyond what a resume and existing recruiting systems can provide."  One of the most valuable lessons learned from PARC is that it's possible to find the find candidates who excel at unlearning, learning, defining and diligently pursuing their learning roadmaps that lead to reinventing their skills, strengths, and marketability.


Source: HOB