We study the U.S. Operations Research/Industrial-Systems Engineering (ORIE) faculty hiring network, consisting of 1,179 faculty origin and destination data together with attribute data from 83 ORIE departments. A social network analysis of faculty hires can reveal important patterns in an academic field, such as the existence of a hierarchy or sociological aspects such as the presence of communities of departments. We first statistically test for the existence of a linear hierarchy in the network and for its steepness. We find a near linear hierarchical order of the departments, proposing a new index for hiring networks, which we contrast with other indicators of hierarchy, including published rankings. A single index is not capable to capture the full structure of a complex network, however, so we next fit a latent exponential random graph model (ERGM) to the network, which is able to reproduce its main observed characteristics: high incidence of self-hiring, skewed out-degree distribution, low density and clustering. Finally, we use the latent variables in the ERGM to simplify the network to one where faculty hires take place among three groups of departments. We contrast our findings with those reported for other related disciplines, Computer Science and Business.