Algorithm can play matchmaker for jobless

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Posted: Thursday, April 4, 2013 12:00 am | Updated: 11:43 pm, Mon May 19, 2014.

When Monique Nyampong graduated from Long Island University last May, she wished she had a headhunter who knew of employers with openings that would be right for her.

She got the help she needed in the form of a patented computer algorithm developed by AfterCollege Inc., an online job board that analyzed 12 years’ worth of data it has stored about its users and business clients. A month after registering her profile, she found a management-training opportunity with Consolidated Edison Inc. sitting in her inbox. She’ll start the job in June.

“I felt like it gave me specifically what I wanted, like it was catered to me,” Nyampong, 25, of Queens, N.Y., said of the service.

Job-search services like AfterCollege are racing to develop software that thinks and acts like a human recruiter, examining what kinds of workers and companies are drawn to each other so a computer can recommend vacancies to job seekers and candidates to hiring managers. With unemployment at 7.7 percent, more than 12 million Americans are looking for work while almost 4 million openings go unfilled, according to data from the Bureau of Labor Statistics. Targeted matches may help the labor market work better, according to Alvin Roth, an economist at Stanford University near Palo Alto, Calif.

“There are all kinds of inefficiencies that these firms are trying to solve,” said Roth, who shared last year’s Nobel Prize in economics for his research in matching markets. “It might be hard for me to know about the job that’s available. There might be some skill you’re looking for but you might not find me.”

San Francisco-based AfterCollege and competitors such as CareerBuilder Inc. and Burning Glass are taking advantage of mountains of information they have stored over the years. Using resumes that job seekers registered, the services extracted data such as the degrees and certifications workers had acquired, their geographic locations, past job titles and previous employers and when and how long they held positions. From the job descriptions employers posted, the services also collected and organized information such as job title, employer, education and experience requirements of positions as well as the benefits they list.

Engineers then looked for patterns. What kind of worker tends to view, apply to and eventually land what kind of jobs? What kind of employer typically targets what kind of candidate?

That analysis forms the basis of the computerized recommendations. When a job seeker like Nyampong tells AfterCollege that she graduated in May 2012 with a joint bachelor’s and master’s in accounting from Long Island University, and the website tracks her clicking into job advertisements A, B and C, AfterCollege’s computers use their memory of job leads that proved to be good for people like her in the past to find similar positions for her now.

“A traditional headhunter will connect people with jobs, but we’re using data to do it automatically,” said Roberto Angulo, AfterCollege’s CEO. “We know some things about people based on past behavior, and so we can do a good job predicting what are the good opportunities for you to check out.”

Jobless workers receiving government help finding employment are benefiting from the data-driven approach as well. Boston-based Burning Glass, which provides technology for public job-matching programs in states including New York, New Jersey and Oklahoma, coded 60million people’s resumes to teach its software how people transitioned from one job to the next.

That allows the computer to recommend likely next steps for the unemployed, said Chief Executive Officer Matt Sigelman.

AfterCollege’s Angulo was so confident in his improved algorithm’s powers that last year he got rid of the search box on AfterCollege’s homepage.

Job seekers like Nyampong now fill out a profile of themselves instead, trusting the computer to do a better job than their own searches using keywords.

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