Resume Screening & Job Matching System: A Machine Learning Approach

Abstract

A Machine Learning and Natural Language Processing (NLP) Powered Resume Screening and Job Matching System is an indispensable tool in current hiring. Manual screening of hundreds of resumes takes too much time and is liable to bias, especially in this highly competitive era of job-hunting. Companies can enhance productivity, decrease the time-to-hire, and make more impartial hiring decisions with the automation process. The system would scan resumes, extract pertinent information like qualifications, skills, experience, and certifications, and cross-check them against job descriptions for compatibility scores. This would help recruiters immediately filter out the most appropriate candidates without manually sifting through each resume. This will give rise to an efficient system of matching the requirements of an employer with the ability to do so by an individual in the present highly competitive job market. Resume Screening and Job Matching System-Applying machine learning with natural language processing-an Automated Candidate Selection Process. This allows the system to parse, analyze, and bring up the core qualifications, skills, and experiences contained within the resumes and to find alignments with the descriptions in a posted job, thereby yielding compatibility scores. These aim at improving hiring efficiency and reduce time-to-fill along with minimizing bias. Through the use of NLP, the system extracts and processes the pertinent features like skills, experience, qualifications, and job preferences, allowing for effective comparison of job requirements and candidate profiles. Machine learning classifiers subsequently assess the compatibility of candidates and job roles, generating compatibility scores that enable recruiters to make informed choices. Word embeddings, TF-IDF, and deep learning models like BERT and LSTMs also enhance text comprehension and semantic matching, resulting in more precise recommendations.