The problem that algorithmic hiring is designed to 'fix' is...well, not entirely clear. The failure rate of new hires has not spiked (to the extent there are that many new hires to begin with), the sources of potential hires is not exactly a secret no matter how obscure the location and the information about individuals on the job market has never been more extensive or the details more intrusive.
The issue seems to be tied to an increasingly popular but largely delusional notion that 'perfect' candidates for various positions can be identified, thereby increasing effectiveness, productivity and innovative output.
The problems with this mindset are manifest. First, the notion that more meticulous matching of education, training, experience and skills development with available positions somehow optimizes the efficacy of the process is largely spurious. Skills and background are certainly part of the equation. But so is personality, work ethic, determination and a bunch of other intangibles that may defy statistical logic. Anyone who has ever worked in an office can tell you how utterly incomplete skills-matching is in assessing whether someone will be a good co-worker or not. Secondly, the belief that an algorithm can be written to eliminate all of the mistakes made by human search experts and interviewers defies logic. The world is changing too fast and the line between 'need' and 'want' grows ever thinner.
There may well be a role for the sort of data-aggregation software to assist organizations find the people they need. But it is a tool, not a substitute for good judgment. JL
Jeff Roberts reports in GigaOm:
Professionals are generating an ever-growing pool of public data that sends signals about their skills — and their availability. A start-up has made a business of parsing that data for tech firms, and now wants to expand to academe and other professions.
Workers in fields like technology and academia are posting more information about their professional lives online, creating a pool of public data that can be machine-sifted to find job candidates.
That’s the idea behind Entelo, a start-up that believes algorithms can replace much of the heavy lifting performed by recruiters and HR departments. The San Francisco-based company, whose clients include Yelp and Square, parses millions of data points to create what amounts to a “professional graph” for thousands of skilled employees.
As my colleague Derrick Harris explained, Entelo’s data-aggregation software combs through sites like Github and LinkedIn to find job candidates who are likely to be not just qualified, but also available (a burst of online activity is one of the strongest signals someone is ready to move).
Entelo now has over 80 paying clients and ten full-time employees, and on Wednesday it announced a $3.5 million funding round led by Battery Ventures with the participation of Menlo Ventures. The company will use the cash to expand its engineering operations and, eventually, to push into new professional verticals beyond tech.
In a phone interview, CEO Jon Bischke explained: ”The recruiting industry is broken because some people don’t know companies are looking to hire them while others are being constantly being bothered by recruiters when they don’t want to move.”
Bischke says Entelo’s value is easy to demonstrate to clients because they can simply contrast its ROI versus other forms of recruiting. As for pricing, the company rejects the “pay for success” model typically used by head-hunters; instead, it charges clients $6,000 per license to use its search engine and predictive analytics tools.
The most intriguing part of Entelo’s business may be its potential for expansion to different professions. Bischke says the company’s next vertical will be academia because professors are creating large pools of public data about themselves on sites like ResearchGate and Academia.edu. He also predicts that algorithm-based hiring will eventually become part of most white collar professions.
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