Transforming HR with Machine Learning: Data-Driven Strategies for Talent Management

Human resource management (HRM) deals with personnel management in a given firm or organization and is strategic in ensuring that organizations retain their competitive advantage in today’s business environment. The present research work revolves around studying the use of machine learning (ML) in employment and focusing on the analytical approach to HRM. Various methods of ML will be used in the study, such as predictive analysis (PA), natural language processing (NLP), clustering, and recommending systems (RS) to help in recruitment, performance appraisal, employee categorization, and career planning. The data set is gathered from a multinational corporation and contains rich and detailed participants’ records for five years, which greatly contributes to designing and testing the models. The predictive models, particularly the random forest algorithm, demonstrated superior performance in forecasting employee performance and turnover, achieving an Area under the receiver operating characteristic curve (AUC-ROC) of 0.90 and a log-loss of 0.35. The BERT-based NLP model effectively analyzed textual data from resumes and feedback, achieving a Brier score of 0.15. Clustering algorithms, specifically k-means, provided well-defined employee segments with a silhouette score of 0.62. The collaborative filtering-based recommender system excelled in providing personalized training and career path recommendations, evidenced by a normalized discounted cumulative gain (nDCG) score of 0.78. Altogether, the study points to the fact of providing for ethical concerns when employing ML in the sphere of HR, including data privacy and algorithmic bias. Lastly, the findings present an argument that is not only significant in improving the efficiency and effectiveness of the current advanced model and ML-based models but also offers integration solutions to the problems and challenges of implementing ML approaches in HR practices benefiting all organizational customers, current and potential, as well as the overall organization.