Quantitative Research Analyst, Education & Employment (Remote Eligible)

About the role

Mathematica applies expertise at the intersection of data, methods, policy, and practice to improve well-being around the world. We collaborate closely with public- and private-sector partners to translate big questions into deep insights that improve programs, refine strategies, and enhance understanding using data science and analytics. Our work yields actionable information to guide decisions in wide-ranging policy areas, from health, education, early childhood, and family support to nutrition, employment, disability, and international development.

Qualifications

• Bachelor's degree in public policy, education policy, economics, sociology, psychology, statistics, data science, social science, or a related field, or an equivalent combination of education and experience.
• A master's degree or progress toward a master's degree is preferred for candidates whose experience is primarily academic.
• Coursework, training, or experience in research methods, program evaluation, policy analysis, statistics, economics, quantitative social science, data science, or a related area.
• Demonstrated interest in education, employment, workforce development, training, or related social policy research.
• Experience working with data, such as cleaning data, assessing data quality, conducting descriptive analyses, preparing tables, producing charts, or documenting analytic decisions.
• Familiarity with one or more statistical programming languages or analytic tools, such as R, Python, Stata, SAS, SQL, or similar tools. R or Python experience is preferred.
• Strong written and oral communication skills, including the ability to summarize complex information clearly and translate analytic findings for non-technical audiences.
• Strong attention to detail, organizational skills, and ability to manage multiple tasks and deadlines in a collaborative environment.

Preferred Qualifications

• Experience conducting statistical analyses of survey, administrative, program, or longitudinal data.
• Coursework or experience in one or more advanced methods such as causal inference, experimental or quasi-experimental design, regression analysis, survey methods, sampling, nonresponse weighting, performance measurement, longitudinal data analysis, predictive modeling, or data visualization.
• Experience contributing to written research products, such as proposals, client memos, technical reports, issue briefs, literature reviews, academic manuscripts, or presentations.
• Experience or interest in technical assistance, training, or capacity building with public agencies, education organizations, workforce agencies, grantees, or community partners.