As Artificial Intelligence (AI) and Machine Learning (ML) become increasingly integrated into Human Resources (HR) processes, the demand for transparency and fairness in automated decision-making has grown significantly. The black box nature of complex AI and ML models demands an explanation for the decision makers to have trust in the system. This research explores the application of Explainable AI (XAI) techniques and specifically counterfactual generation algorithms to enhance interpretability and accountability in HR analytics focused on employability prediction and job retention. Using a real-world HR dataset, we develop and evaluate machine learning models to predict an individual’s likelihood of being hired and their potential for long-term retention. To make these predictions more transparent, we generate counterfactual explanations that highlight the minimal changes needed in candidate attributes to alter the model’s unfavourable outcomes. These insights provide HR professionals and applicants with actionable feedback, support data-driven policy development, and help identify potential biases in the recruitment and retention process. Our findings demonstrate that integrating counterfactual reasoning into HR analytics not only improves model interpretability but also helps in providing an insight into the dataset and practices.