Depression commonly emerges during adolescence and is conservatively estimated to affect up to 12.5% of 12- to 17-year-olds annually (Clayborne, Varin & Colman, 2019). Prior longitudinal analyses have identified significant heterogeneity in the level and growth of depressive symptoms during the transitional period from adolescence to young adulthood. The purpose of this study was to follow one representative cohort during this transition to identify non-cognitive, in-school risk factors for atypical depression trajectories and contextualizing them using impactful labor market outcomes. Latent growth mixture modeling (GMM) was used to assess and classify depressive symptom change trajectories using four occasions of measurement from 1994 to 2008. The study used the public-use dataset from the National Study of Adolescent to Adult Health (Add Health). Two distinct change trajectories were identified using a latent basis model and classified as being either Normative (82.2%) or Elevated (17.8%) in its symptom level and shape. The adolescents in the Elevated class exhibited elevated and increasing depressive symptoms, while the Normative class showed consistently lower and decreasing depressive symptoms. Several demographic factors—being female, Black, or Native American—were risk factors for membership in the Elevated class. In addition, four non-cognitive, within-school indicators were associated with a significantly higher risk for an Elevated classification. The strongest non-cognitive risk factor was low levels of school connection, followed by high delinquency, low self-perceived likelihood for college admission, and retention in grade. Lastly, adults who were classified as Elevated in their depressive symptoms reported significantly lower socioeconomic outcomes across all eleven labor market indicators, including measures of employment benefits, job satisfaction, income, and public assistance.