Machine Learning approach for hiring demand forecasting in Large Scale Organizations

In the field of human resources management, the ability to forecast hiring demand with precision is critical for optimizing workforce planning and talent acquisition strategies. As organizations become increasingly complex, traditional forecasting methods, such as simple time series models or heuristic approaches, often fall short of capturing the multifaceted nature of hiring dynamics. In large multinational corporations, forecasting hiring demand requires the consideration of various factors, including macroeconomic indicators, organizational structure, and workforce fluctuations. This thesis proposes the development of a sophisticated machine learning workflow to enhance the accuracy and reliability of hiring demand predictions.