Modeling Career Path Trajectories

Understanding the structure and dynamics of the job market is important both from the local perspective of individual job hunters and from the global perspective of economists and policy makers. In this paper, we explore such questions by analyzing the text of a corpus of resumes and their job transitions. We first demonstrate the use of a statistical topic model to discover the latent skills that make up each job description and to map the cooccurrence patters of various skills. Although these topical features alone are good at discovering the structure of the job market, they are relatively poor at predicting job transitions. We next present a topical sequence model trained on topic features that has improved ability to predict subsequent job titles.