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Using the NLSY79 and NLSY97 longitudinal datasets, this paper documents the adolescent skill (di)convergence by gender over the last four decades in terms of math, verbal, science, mechanical, and social skills and estimate machine learning models to identify the skills, high school course-taking and family background characteristics that are most predictive of one's educational attainment, choice of college major and occupation. I distinguish among non-STEM, pure-STEM and applied-STEM majors, as the pattern of female entry into pure-STEM and applied-STEM categories has been quite different. A nonparametric decomposition to empirically assess the relative importance of different factors is also performed. The results show that with men and women converging on math, administrative, science, and mechanical skills among the lower quintiles, the younger cohort displays less effect of skill gaps on major and occupation segregation. Although mechanical skills are found by the literature to constitute mainly the low-skilled workers' human capital, this study finds that conditional on having a four-year college degree, the level of one's mechanical skills is the most predictive factor of whether one chooses the applied-STEM fields, the most lucrative of all fields within this non/pure/applied-STEM taxonomy.