Required Experience: Demonstrated expertise in at least two of the following areas: causal inference and quasi-experimental designs, cost-benefit analysis, advanced predictive modeling, longitudinal and panel data analysis, ROI forecasting, analysis of large-scale assessment data, machine learning applications, labor market information and job-postings analytics, linkage and analysis of administrative wage-outcome data systems, policy simulation, microsimulation modeling, and structural dynamic modeling. Particular areas of interest include (1) understanding and measuring the skills and capabilities that drive success in education and labor markets, including how advances in AI are reshaping skill demand, workforce readiness, and pathways to opportunity, and (2) generating evidence on the effectiveness of educational and workforce development pathways, including competency-based education (CBE), career and technical education (CTE), and other approaches designed to promote skill acquisition, employment, earnings growth, and long-term economic mobility.