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| Photo: John Mannes |
"There’s absolutely nothing efficient about
sorting through 30,000 resumes by hand. Recruiters spend months
evaluating applicants only to have great prospective candidates get lost
in the pile." reports John Mannes, writes about machine learning and AI for TechCrunch.
On the stage of TechCrunch’s Startup Battlefield, French
startup Riminder
made the case for how its deep learning-powered platform could augment
recruiters — helping them better surface ideal contenders for job
openings.
Riminder generates candidate rankings for open jobs by comparing applicant resumes against resumes from current employees and others in the world with similar job titles. Behind the curtain, Riminder uses a cocktail of computer vision and natural language processing to build profiles of what ideal resumes should look like for specific roles.
Once a resume is processed, recruiters can view its strengths and weaknesses. Riminder makes it easy to see this information visually and to identify market trends, like the most popular schools to recruit from or the most common skills applicants for a certain type of job typically have. The goal is to make sure recruiters have the information they need to judge candidates on both their ability to fit into company culture and their mastery of key skills.
When demonstrating Riminder’s value to a potential recruiting client, the team often runs tests on historical data. This data makes it easy to compare the platform’s automated short list with a human-generated list.
“When we compared results, recruiters found 3x more candidates they were interested in, they just weren’t using the right keywords,” explained Mouhidine Seiv, founder of Riminder.
Read more...
Source: TechCrunch
