Still, today job and talent search is inefficient, says Jersin: seekers must send out many applications to find a position and recruiters must sort through a high volume of candidates before finding one that is a fit. “We’ve seen efficiency improvements upwards of 50% in our core products for recruiting already-the number of interactions that it takes to find a candidate that engages in the process, or reaches a certain stage, or find a candidate that gets hired.” In recruitment we are heading in the same direction.”Īt Linkedin, AI is used to sift through job-seeker and recruiter data alike, as well as information on the company’s 500 million-plus registered users, says John Jersin, senior director of product management for the company. “They make the call whether they will operate or not. Today they have the means to gather greater levels of data on what ails their patients, but the big decisions are still made by humans. “Surgeons 50 years ago had an X-ray picture, and based on an X-ray picture they decided if they would operate,” he explained. The human element, he noted, will still be needed to make the final decision on hiring. Raaff also says the role of recruiter will become easier as the means to gauge talent will be far more robust. Through the application and testing process, job applicants get a sense of what to expect from a job, and Raaff says that in the future if an applicant is rejected for a job, Harver will suggest positions at other organizations for which he or she is better suited. Harver began by focusing on high-volume job sectors like call centers and customer-facing roles, and has now begun catering to smaller companies’ recruitment needs. The firm designs algorithms and testing processes with recruiting clients to predict a good match for a job. It’s also impossible to predict whether someone would stay in the job and do it proficiently, he added. “A lot of people apply for jobs and they don’t have any clue what the job is really like,” says Barend Raaff, Harver’s founder and CEO. “The more objective data we introduce in technical recruiting-and in recruiting overall-the better all of us will be,” says Sloyan.īarend Raaff, CEO of Harver. There are so many data points to consider: skills, salaries, location, personality, experience, company culture, inaccuracies in resumes or job descriptions. But in the effort to connect workers and employers, complexities abound. This system of skills-based recruiting opens doors for programmers based largely on their proven talents and offers recruiters a definitive look at what a prospective hire can actually do.įor Tigran Sloyan, CodeFights’ co-founder and CEO, looking for work is a classic matchmaking scenario, similar to ones being facilitated in the transportation market by Uber and in the hotel and short-term rental space by Airbnb. They can connect with prospective employers, who also use the company specifically to find prospective hires. Skills testing is also the centerpiece for CodeFights, a four-year-old San Francisco-based firm that offers tech workers a platform on which to practice their coding skills and to use those skills-which are scored and rated on the CodeFights platform. That means gauging skills at the outset, which would require testing on a job search platform-saving time for both recruiter and job seeker. A career-focused AI should also tell job seekers whether they are being paid fairly at their current job, with a high degree of accuracy, compared to others in their line of work.įor recruiters, says Mukherjee, weeding out incompatible talent is key. As AI and machine-learning develop in the field, a service like Indeed should be able to suggest new, much more compatible opportunities based on a job seekers’ work experience, skills, salary, interests and location. This information would then be compiled to make it easier for recruiters to evaluate.Īs for the future, Mukherjee says job seekers will likely see a reduction in their research time while looking for work. “It would sort things that the sentence implies.” Listed skills on a job seeker’s resume would be recognized and set aside, he explained, as would companies worked for, years of experience and other elements. “It would look at a sentence and try to make sense of that sentence,” says Mukherjee. This is tricky because text data can be relatively unstructured. Indeed’s AI also pulls information from resumes for recruiters using something called natural-language processing, which involves extracting relevant words and phrases from text using computer programs.
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