Research Summary The current study used latent class analysis (LCA) to identify profiles of criminogenic needs in a sample of 17,252 community‐supervised individuals from one state's probation system. The purpose of this research was to illustrate the complexity of offender need profiles to inform the development and implementation of correctional interventions. The LCA analyses revealed four classes of dynamic needs. Conditional item probabilities were examined to label the four classes based on their likelihood of presenting with static risk, criminogenic needs, and destabilizing factors (i.e., factors that indirectly relate to recidivism). The four classes were characterized by the following: a low probability of both risks and destabilizers (LN‐LD), a moderate probability of risk and criminogenic needs with a high probability of multiple destabilizers (MN‐HD), a high probability of risk and needs with moderate probabilities of destabilizers (HN‐MD), and a high probability of static and criminogenic needs and destabilizers (HN‐HD). Finally, the relationship between latent class membership and three separate recidivism outcomes was assessed. Consistent with study hypotheses, individuals in latent classes characterized by a greater probability of criminogenic needs and lifestyle destabilizers were more likely to experience subsequent criminal justice involvement, regardless of risk level. Policy Implications Simplifying the complexity of offender risk and need profiles through empirical classification has direct implications for policy and practice. First, it clarifies whether dynamic needs and/or risk should drive decision making. Second, the integration of dynamic risk factors into the case management process can inform strategies to mitigate static risk and inform the development of new and improved interventions. The current study findings provide insight into the clustering of dynamic risk factors within individuals. This classification structure has the potential to increase the precision of case management decisions by identifying targets for programming that are likely to co‐occur for many offenders. Specifically, programs can be developed to tailor components to specific static risk and need profiles.