The state-of-the-art, machine learning technology of Tilr drives the ongoing unemployment crisis as a result of the COVID-19 pandemic and follows the American Workforce Policy Advisory Board initiative to employ workers on the basis of talent and make sure a more equitable hiring process.
Tilr’s revolutionary machine learning algorithm is based on unique ‘skills-matching’ technology that uses weighted skills, relevant occupations and other job preferences well-kept to serve the public as well as private sectors, incorporating part-time and full-time workers, with a web app willingly available for businesses and governments, and a mobile application for job-seekers.
Tilr’s new version, combined with years of inputs from past matches helps the technology to learn intuitively in order to make sure the best possible job match.
Organizations will be able to select from a labour marketplace of readily available workers and perceive where skills lie ‘in-house’, sparing in both HR time and costs while eliminating bias in the onboarding process.
Job-seekers can enter their skills through the intuitive application of Tilr or by using Tilr resume-to-skill mapping technology. They will have the option to then match their skill-sets with the positions best suited in real-time and further, discover opportunities conveniently placed close to them and accommodating to their schedules.
Stephen Shefsky, Co-Founder and CEO, Tilr, stated that “We are pleased to announce the launch of new skill-matching and skill-mapping technology of Tilr. Tilr provides a fast, efficient and the user-friendly alternative to antiquated keyword searches on job boards. This is the pinnacle of investment of our Team in and commitment to constant technological originality, differentiating ourselves in automating and enhancing the onboarding experience, one in my view that was already inefficient in bringing jobs and job seekers together.
Our experience gathered over the past 5 years has delivered more than 100,000 matches from our organization’s inception, working alongside more than 500 client companies and over 50,000 job seekers in the process. Our algorithm will constantly improve and learn, recognizing candidates faster and with higher job-matching accuracy than any other technology present in the marketplace.”
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Aashish is currently a Content writer at Martech Cube. He is an enthusiastic and avid writer. His key region of interests include covering different aspects of technology and mixing them up with layman ideologies to pan out an interesting take. His main area of interests range from medical journals to marketing arena.