Job scams have emerged as a rapidly growing form of cybercrime that manipulates human decision-making processes. Existing countermeasures primarily focus on scam typologies or post-loss indicators, offering limited support for early-stage intervention. In this study, we examine how behavioral decision signals can be operationalized as computational features for identifying vulnerability-associated signals in job fraud. Using anonymous survey data collected from a university population, we analyz...