In this webinar, our panelists will explore how interdisciplinary training can broaden research possibilities and open diverse career pathways. The session highlights two complementary experiences: how a PhD student in Computational Social Science within the College of Engineering integrates sociological theory into his dissertation research, and how a Sociology PhD student incorporates computational and data-driven methods into his dissertation. In other words, how can we build bridges between sociology and computational engineering? A second objective of the event is to showcase the kinds of careers that emerge from this interdisciplinary training. Our panelists will discuss the roles they currently hold, and how the skills, methods, and conceptual frameworks gained during graduate school have shaped their professional trajectories. We hope to illuminate job pathways for scholars who combine sociology and computational social science. To guide the discussion, we will explore questions such as: • Why should a Sociology PhD student take courses in computational social science? • Why should a Computational Social Science PhD student take courses in sociology? • How did each benefit from pursuing this cross-training? • In what ways has interdisciplinary experience influenced their research and work after, or beyond, the PhD?