Quickly find the ideal candidate
It’s no secret that finding good workers isn’t easy, particularly in the competitive tech world. Hiring managers can spend hours using primitive keyword search tools to sift through half-relevant resumes on job boards, and workers with in-demand skills get bombarded with emails from recruiters offering them jobs they’re not particularly interested in, says Ed Donner, cofounder and CEO of New York startup Untapt .
Hiring made easy
“Hiring tech people is an incredible pain point,” says Donner, who previously headed a technology team with hundreds of employees at JPMorgan Chase. “It’s still impossibly hard to find talent.” This Is How AI Will Change Your Work In 2017 Untapt is one of a number of companies looking to make it easier to digitally dig through piles of resumes, using machine learning techniques to develop algorithms that predict how well-suited a candidate is for a job. Advocates and industry experts say that adding automation to recruiting can save time and money and can potentially help hiring managers find and consider a more diverse set of applicants.
Circumvent biases
“People are very subject to unconscious bias, and with the help of the machine, you can overcome a lot of the unconscious bias,” says Tom Haak, director of the HR Trend Institute . “When the machine learns what you’re looking for, it can say, if you find this type of candidate positive, here are other ones.” Untapt, which launched in early 2015, is focused on filling financial tech positions, and its software is designed to locate candidates with particular technical skills more efficiently than could be done through traditional text searches of resumes and cover letters. If a company is looking to hire developers adept at functional programming, a software development technique popular in parts of the fintech world, Untapt’s tool can learn to recognize that experience in certain programming languages is likely a good sign, Donner says. […]
read more – copyright by www.fastcompany.com
Quickly find the ideal candidate
It’s no secret that finding good workers isn’t easy, particularly in the competitive tech world. Hiring managers can spend hours using primitive keyword search tools to sift through half-relevant resumes on job boards, and workers with in-demand skills get bombarded with emails from recruiters offering them jobs they’re not particularly interested in, says Ed Donner, cofounder and CEO of New York startup Untapt .
Hiring made easy
“Hiring tech people is an incredible pain point,” says Donner, who previously headed a technology team with hundreds of employees at JPMorgan Chase. “It’s still impossibly hard to find talent.” This Is How AI Will Change Your Work In 2017 Untapt is one of a number of companies looking to make it easier to digitally dig through piles of resumes, using machine learning techniques to develop algorithms that predict how well-suited a candidate is for a job. Advocates and industry experts say that adding automation to recruiting can save time and money and can potentially help hiring managers find and consider a more diverse set of applicants.
Circumvent biases
“People are very subject to unconscious bias, and with the help of the machine, you can overcome a lot of the unconscious bias,” says Tom Haak, director of the HR Trend Institute . “When the machine learns what you’re looking for, it can say, if you find this type of candidate positive, here are other ones.” Untapt, which launched in early 2015, is focused on filling financial tech positions, and its software is designed to locate candidates with particular technical skills more efficiently than could be done through traditional text searches of resumes and cover letters. If a company is looking to hire developers adept at functional programming, a software development technique popular in parts of the fintech world, Untapt’s tool can learn to recognize that experience in certain programming languages is likely a good sign, Donner says. […]
read more – copyright by www.fastcompany.com
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