A Talent-sourcing platform that delivers the right jobs to the right People

Abstract

In the modern job market, both employers and job seekers face numerous challenges, including skill mismatches, ineffective job search strategies, and time-consuming recruitment processes. To address these issues, this paper presents a machine learning-based talent-sourcing platform designed to improve the efficiency of job matching by analyzing both candidate resumes and job postings. The platform uses supervised machine learning models combined with Natural Language Processing (NLP) and Named Entity Recognition (NER) techniques to create personalized job recommendations based on compatibility scores. This data-driven approach not only minimizes biases but also promotes diversity and inclusion within the recruitment process. The platform leverages real-time feedback and ongoing data collection to continually refine its predictive capabilities. This paper outlines the platform’s underlying methodology, implementation of custom models, and its potential to revolutionize talent acquisition by delivering more relevant, unbiased job matches for both employers and job seekers