We propose a potential solution to the socio-economic challenge of understanding professionals’ decisions to change or leave their jobs prematurely. This issue is particularly relevant from a human resources perspective, aiming to identify and analyze factors that influence early career transitions among employees in specialized roles. Our approach uses machine learning models—Logistic Regression, Decision Tree, Random Forest, and XGBoost—to predict job change behavior. To enhance interpretability, we apply SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), providing insights that can support human resource decisions and reduce potential financial losses. Among the models tested, SHAP offered more consistent and meaningful explanations than LIME across the framework.