Data mining and analysis in headhunting: Applying Artificial Intelligence in Recruitment and Executive Talent Search

This thesis explores the application of data mining and analysis in headhunting, with a particular focus on modern uses of artificial intelligence (AI) and machine learning (ML). The aim is to examine how advanced data methods and AI tools can optimize recruitment processes, especially in identifying and evaluating talent for executive and specialized positions. The thesis provides an analysis of current trends, an overview of available data mining methods, and the benefits of integrating AI into executive search. The practical part includes an experimental application of selected AI models on real or simulated datasets. The research demonstrates how AI can be employed for predictive analytics, automated candidate selection, and personalized approaches to recruitment. Special attention is given to the ethical and legal aspects of data use in recruitment processes, including the issues of algorithmic bias and data protection. The findings confirm that AI technologies and data mining provide significant advantages in improving the efficiency and accuracy of recruitment processes. The thesis also offers recommendations for implementing these technologies in practice and discusses potential future developments in this field.