Taylor wants to get that rate higher.
“Instead of receiving 20,000 applications to make 100 hires, you’ll just need 1,000,” he says of the future. “And for the candidate, you don’t just have to apply for 50 companies. You can apply for three positions that are appropriate.”
Naturally, curating thousands of candidates will come courtesy of machine learning algorithms that Debut is set to begin developing this summer.
The company’s developers will start by analyzing patterns of people going through the selection process across dozens of its corporate clients (of which Taylor hopes to have 110 by the end of 2018).
It’ll then use that information to make personalized recommendations and predictions.
“One day you’ll come on the app and it’ll say, ‘Here are seven jobs we know you will perform really well through selection, based on thousands of other people.’” he says. “We’ll apply for you. You’re in your bedroom, and it doesn’t matter how wealthy you are or how many connections you have. The app will give you five or six employers who will want you, and you just tap, ‘I approve’ and come back on the app to go to the interviews.”
Taylor describes university degrees as a “commercial commodity” that are becoming irrelevant to employers—not just Ernst & Young. “We want to unbundle that and turn our user base into a behaviour- and competency-based user base,” he says. “The strength would be the person’s competency as opposed to academic success.”
Debut also plans to use its new funding to court small-to-medium-size businesses. It'll take candidates that almost made it through the interview process with a larger company like Google or Tesla, and match them with the smaller clients.
Its latest funding round was led by British entrepreneur James Caan, job site Indeed’s cofounder Paul Forster and included Saul Klein’s LocalGlobe in London.