About the Position

Dexis is seeking Senior Data and Economics Lead/Senior Researcher to support anticipated programming with the Department of Labor (DOL).  This opportunity will support the Employment and Training Administration (ETA) in providing comprehensive operational, analytical, and program support services to develop and support the Artificial Intelligence Workforce Hub. 

This position is contingent upon contract award. 

Responsibilities

• Leads the Hub’s analytical work by identifying and prioritizing research directions, conducting applied economic and quantitative analysis, and translating findings into policy-relevant insights. 
• Evaluates the usefulness, quality, comparability, and methodological soundness of partner-contributed data and analyses to determine fitness for use in Hub outputs.  
• Integrates partner and federal data where appropriate and adapts analytic approaches based on evolving data availability and constraints.  
• Monitors and responds to changes in external research literature to inform ongoing analysis and research direction.  
• Develops end-to-end analytic pathways from question identification to empirical analysis, balancing research judgment, quantitative rigor, and execution speed.  
• Navigates partner data limitations and sensitivities by identifying pragmatic analytic alternatives (e.g., aggregation, privacy-preserving, or constrained-access approaches) and exercising judgment on usability thresholds.  
• Communicates clearly with Government staff, partners, and research stakeholders on findings, methodological limitations, analytic approaches, and appropriate caveats for constrained or sensitive data.  
• Applies expertise in labor market dynamics and applied economic analysis; experience assessing technology’s economic effects is preferred. 

Qualifications 

• A minimum of eight (8) years of relevant professional experience conducting applied quantitative research, economics research, labor market analysis, policy analysis, or related work using administrative, survey, platform, or other complex datasets.
• Demonstrated proficiency in one or more statistical programming languages such as R, Python, Stata, or similar tools; demonstrated experience designing and executing empirical analyses under time constraints. 
• Demonstrated experience assessing data quality, methodological limitations, and fitness for use across heterogeneous data sources. 
• Demonstrated ability