Despite the recent efforts to investigate crash severity, worker presence and its impact on injury severity in work zone crashes is still unexplored. A better understanding of work zone crash characteristics can help to enhance roadway safety for not only road users, but construction crew. Employing a mixed logit (MXL) modeling framework, the present study aims to identify and investigate contributing factors associated with work zone crashes involved workers. Random forest (RF), a data mining approach, is also applied to evaluate variables’ importance for comparison between worker-involved and non-worker involved crashes. The estimation results demonstrated that work on the shoulder or median, advance warning area, daytime non-peak, and multi-occupant variables have heterogeneous effects on injury severity. The analysis of marginal effects indicated variables with substantial influences on injury severity outcomes. The results of this study provide valuable insights for work zone crashes analysis.