Predicting employees' career moves benefits both talents with career planning and firms in preparing for the gain and loss of human capital. In this paper, we follow the categorization theory to design an explainable AI artifact for the employee occupational mobility prediction problem. Under a coherent categorization theory framework, three theory-driven components explain different mobility mechanisms. The experimental results approve the effectiveness of this theory-driven approach compared to state-of-the-art baselines in terms of occupational mobility prediction.
