Attrition prediction research typically focuses on constructing models that involves one observation per employee over a limited time period, while the rest of the employees are discarded. Time-series attributes are transformed to non-time-series ones by applying statistical operations (e.g. sum, max, etc.). Such methods result in information loss and therefore less effective predictions. In this paper, we introduce a dynamic approach to employee attrition prediction, leveraging the longitudinal nature of the data, and allowing the models to generalize across behaviors and providing a closer estimate of the employee risk of leaving.