We designed a sophisticated model to predict whether a business process for hiring candidates for a specific set of positions was on track to complete successfully or needed increased attention.  Throughout the process, the model predicted the probability of successfully hiring all N positions within the required time limit.  The model was based on Continuous-time Markov Processes from Queuing Theory, and modeled the business process as a probabilistic state machine.  State transition probabilities were learned from historical data. A limited, early proof-of-concept was implemented in Python to bootstrap the implementation team.