A Machine Learning Approach for Automated CV Data Analysis and Job Profile Identification

Abstract

Human resources is an important department in each organization as it manages the life cycle of employees from recruitment, training to retirement or termination of contracts. The recruitment process starts with a job opening, followed by a selection of the best fit candidates from all applicants. Matching the best profile for a job position requires manually reviewing numerous CVs, which is time-consuming and can sometimes result in suboptimal candidate selection. This paper aims to reduce the workload of HR personnel by automating the preliminary stages of candidate screening, thus enabling a more streamlined recruitment workflow. This tool introduces an automated system designed to help with the recruitment process by scanning candidates' CVs, extracting pertinent features, and employing machine learning algorithms to decide for the most fitting job profile for each candidate. Our approach utilizes natural language processing (NLP) techniques to identify and extract key features from unstructured text in CVs, such as education, work experience, and skills. Subsequently, the system utilizes these features to match candidates with job profiles, leveraging the power of classification algorithms.