Smart Talent Sourcing Through Advanced Skill Profiling Technique

Abstract

The hiring process often struggles with aligning job seekers' skills to employers' requirements, leading to inefficiencies and mismatches. To address this challenge, a dual-functionality system is proposed that leverages Natural Language Processing (NLP) techniques, including BERT for embedding textual information and cosine similarity to rank resumes according to their alignment with job descriptions and to recommend suitable candidates to employers and vice versa. The primary goal is to enhance the accuracy and efficiency of job-to-job-seeker matching by integrating these advanced methods as features within the model, alongside other relevant data points. The developed system effectively addresses challenges such as noisy data, heterogeneous sources and multilingualism, demonstrating its potential in improving the hiring process with increased accuracy and precision of the system. These findings suggest that the proposed method not only streamlines talent acquisition but also offers broader applications in talent management systems, ensuring more precise and efficient matching of candidates to job opportunities. The implications of this research extend to enhancing the recruitment process's overall effectiveness and providing a robust foundation for future advancements in AI-driven talent management.