A Deep Learning Framework for Optimizing Talent Acquisition and Placement

Talent acquisition and placement are critical processes in human resource management, yet they often face inefficiencies due to manual screening, bias, and mismatched hiring decisions. Finding suitable candidates for an open role could be a daunting task, especially when there are many applicants. It can impede team progress for getting the right person on the right time. In traditional models, they use ML technologies like KNN, and NLP on text based screen resuming which has limitations such as a bias, inefficiency and lack of personality assessment. To addressing this challenges we proposed a deep learning based framework for talent acquisition and placement using CNN and RNN. Comparing to the traditional models this system enhances improving accuracy, reduces recruiter workload to better workflows placement and talent acquisition. This enhances efficiency, improves decision-making, and ensures optimal talent placement in organizations.