There’s always been an element of randomness to finding a job. Maybe you saw an ad in the paper, or knew someone who knew someone, or sent a lucky email. It could be that you ended up in a career you never would have anticipated, or like one-third of college-educated workers
, a field you didn’t major in. Serendipity isn’t always a bad thing; how often do we really know what we want?
Hans Haringa had no idea what he wanted until Knack -- a Silicon Valley start-up -- told him. The Atlantic Monthly relates how
, by analyzing two decades’ worth of data, Knack was able to show the Royal Dutch Shell
(NYSE:RDS.A) executive what traits he had been unconsciously looking for in new hires: social intelligence, conscientiousness, and a tendency to let the mind wander (and thus stay open to possibilities), for example. Now, Mr. Haringa can “avoid wasting time” on the 80% of applicants who don’t fit that profile. Conversely, 100% of applicants now know what traits to list on their résumé – and if they didn’t, Knack could probably tell them.
So much for serendipity. These days, employers are leveraging data in their quests to find the perfect candidate. Employees are also turning to data in their quests to appear perfect. The result is something like the SAT test we all took in high school: a measure, not of intelligence, but of the ability to afford a prep course.
Consider that 22,000 businesses now use LinkedIn
(NYSE:LNKD) to screen potential job candidates, among other things. No surprise, then, that we have a growing industry of professional profile writers
. Or that job seekers would pay the social network in order to become Featured Applicants, placing their applications at the top of the pile.
There are other, less expensive ways to game the system. Join the right groups. Expand your network. Trade "endorsements" with complete strangers. Avoid the buzzwords that LinkedIn says to avoid
, and claim the skills that LinkedIn says will land you a job
. Optimize yourself, much like you’d optimize a Web page for Google’s
(NASDAQ:GOOG) search engine.
Like people, data can be misleading. Hans Haringa thought that Knack was showing him the ideal candidate – not a reflection of his own bias. Businesses pore through data in order to find the perfect employee, never considering that they might be corrupting that data simply by placing so much importance on it. High school students are using LinkedIn to identify the best college
for launching their future careers, but they’re basing that decision on information from the network, whose user demographic is skewed heavily
toward males, high earners, and specific industries like tech and finance.
And if the past is any indication, 36% of those future college students will regret their choice of major anyway.
We don’t always know what we want, and we don’t always know what we’re measuring. Few 18-year-olds have their lives figured out. On the other hand, there are plenty of mediocre hiring managers who can’t be expected to conceive of, much less identify, a "perfect" job candidate. Some of LinkedIn’s 260 million users will play fair, but in all likelihood, some will not. Imperfect data is empowerimg imperfect people – so why would we expect the results to be anything other than, you know, not perfect?
CEO Jeff Weiner says that LinkedIn’s goal is to "enable capital, all forms of capital – intellectual capital, working capital, human capital – to go to where it can be best utilized and leveraged." And yet the company’s entire business is to reward those who pay, not those who qualify. Like the rest of us, LinkedIn has every incentive (in fact, 25 billion incentives) to fudge its résumé.
It could be that smaller, more specialized networks like Doximity
– favored by medical professionals – are harder to game, and can provide employers with better data. I suspect, though, that many of them will find social media to contain too much misinformation, and they’ll end up back where they started: on job boards, posting and praying, and trusting in serendipity.
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