AI-Driven Job Fit Prediction Using Recurrent Neural Networks in Human Resources Management

Abstract

Human resource management, it is essential that candidates ought to be appropriately well-fitted to appropriate job posts to enhance productivity, reduce turnover, and enhance job satisfaction. The essay proposes a model for Artificial Intelligence (AI)-based forecasting of job suitability based on Recurrent Neural Networks (RNNs) predicting by forecasting job matching based on candidate history and career profiles. The model uses to train processes different features such as demographic data, career history, psychometric test data, and job performance data to forecast job fit for different job positions. The model uses the HR Analytics: Job Change of Data Scientists dataset with employee data, including job satisfaction, education, experience, job titles, and tenure. Using RNNs, the model detects temporal trends and career development of applicants and makes a dynamic and customized job fit prediction. The approach is tested on common performance measures like accuracy, precision, recall, and F1-score and outperforms classical models. The model performed 98.5% accurate, 97% precise, 98% recalling, and 97.5% F1-score, proving its utility in job fit prediction. The AI-powered solution provides a data-driven, scalable solution to improve recruitment, employee-job fit, and facilitate improved long-term career planning.