Riminder: Artificial Intelligence and Technology to Find the Right Employee
The field of human resources is evolving quickly. While recruiters previously spent hours perusing thousands of resumes, they are now discovering these tedious parts of their job can be streamlined with sophisticated artificial intelligence (AI) technology.
Enter Riminder, a new recruitment technology for HR that uses deep learning to sift out candidates who are not likely to be a good match. The AI technology analyzes data while learning with each interaction to find candidates most qualified with the required skill set, culture fit, and career pathway that matches the job title and description. The company’s founder and CEO Mouhidine Seiv says the software can help recruiters hire up to 60 percent faster, with 80 percent fewer interviews.
How Riminder Works
At its core, Riminder’s process seems simple: The AI software compares candidates’ resumes against the resumes of current employees at the company, along with other workers with similar job titles. From there, it generates candidate rankings, permitting recruiters to choose from a short list of applicants to contact for an initial interview.
The AI Technology Behind Riminder
The AI algorithms behind Riminder employ Deep Computer Vision and Deep Natural Language Processing methods to extract personal information, experience, education, skills, and interests, along with a candidates’ identifying information (such as email address, phone number, and past employers).
It then generates a computer model of what an ideal candidate would look like, and compares resumes to the ideal model. The unique Neural Network architecture that comprises the AI solution exhibits reasoning capabilities to understand the evidence behind conclusions, so it’s not just matching facts but applying logic to understand the “why” behind the choices.
Riminder shows recruiters the data points that might make a candidate a good fit, including a particular skillset or a certain college degree. Because Riminder analyzes and compares resumes from all over the world, it can correct for regional discrepancies, helping recruiters consider candidates beyond their own geographic circle to successfully draw from a worldwide talent pool.
The software learns by analyzing job market trends specific to the recruiter’s industry, as well as adjusts assessments based on the recruiter’s feedback so accuracy improves with each interaction.
Advantages for Recruiters and Candidates
Riminder is designed to aid recruiters in the most cumbersome, yet least intuitive aspect of their job. “Talent assessment is very time consuming and ineffective,” explains Seiv. To streamline the process, Riminder uses deep learning technology to analyze millions of career pathways, applicant resumes, and successful employee resumes all in milliseconds.
It also helps candidates refine—or even re-define—their career path by matching people with the best possible job—which may not necessarily be the one for which they applied. In a rapidly changing, increasingly diverse job market, the software helps employees get on the right career path to find a better fit, increase job satisfaction, and improve retention.
The New Role of Human Recruiters
“It’s almost impossible to fine-tune criteria to assess people in a very exhaustive way,” says Seiv, pointing out some of the issues with traditional recruitment technology and techniques. That’s why Riminder strives to eliminate human errors that can occur due to recruiter fatigue or human bias.
It’s important to remember, however, that Riminder is a recruitment “assistant.” This HR technology is designed to allow recruiters to focus on the “human” parts of their job. Recruiters and HR directors can spend more time attracting top talent through innovative recruitment strategies, face-to face interviews, and making final decisions based on a small pool of highly qualified candidates that Riminder delivers directly to the recruiter’s desktop or mobile device.
Photo Credit: HaticiSosyal Flickr via Compfight cc
This article was first published on Converge.xyz