Quantitative Researcher, Human Services; Remote Eligible

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.

Mathematica offers our employees competitive salaries and a comprehensive benefits package, as well as the advantages of being 100 percent employee owned. As an employee stock owner, you will experience financial benefits of ESOP holdings that have increased in tandem with the company's growth and financial strength. You will also be part of an independent, employee-owned firm that is able to define and further our mission, enhance our quality and accountability, and steadily grow our financial strength.

Read more about our benefits here:
Benefits at a Glance.

Mathematica is hiring researchers to join our vibrant and growing Human Services department. We are seeking candidates with expertise in quantitative research methods, causal inference, and policy research to conduct studies and support program evaluation across a range of topics, including TANF, employment, child welfare, early childhood, family strengthening, and economic mobility.

This position supports project teams within the department and across the company in the planning and execution of rigorous, data-driven projects for clients such as the U.S. Department of Health and Human Services, state and local agencies, and Foundations and nonprofits. Mathematica is seeking researchers who are excited to solve complex policy and programmatic challenges in ways that are evidence- and data-driven, and who can agilely adapt to and evolving policy and technology landscape.

The type of work we do includes activities such as:

• Evaluating programs that focus on topics such as healthy marriage and relationship education and teen pregnancy prevention using randomized controlled trials
• Designing and implementing a nationally representative study of supports for family stability in Head Start
• Conducting systematic evidence reviews of studies that focus on home visiting, teen pregnancy prevention, and employment
• Improving child welfare outcomes for children and families, with an emphasis on linking administrative data and strategies to increase permanency and enhance the safety of children who are at risk of out-of-home placements
• Using predictive analytics to improve outcomes for children and families
• Building evidence around effective strategies for helping individuals with low incomes find and sustain employment
• Enhancing and supporting the data analytic capacity of state child welfare and Medicaid agencies to improve the delivery of services
• Advising agencies on evidence-based ways to increase program engagement and learn, innovate, and improve

• Collaborate with senior staff in a multidisciplinary environment to apply rigorous quantitative research methods, including study design, causal inference, data management, and statistical and econometric analysis for describing or evaluating programs and policies
• Learning and applying new methods, tools, and technologies, including AI-enabled tools, data visualization platforms, and emerging analytic approaches
• Designing rigorous mixed-methods evaluations, including primary and secondary data collection
• Applying innovative research approaches and advanced statistical methods to answer policy-relevant questions
• Working with data scientists to link and analyze administrative data
• Helping to develop proposals for new research projects
• Guiding program staff and policymakers in the use of evidence to make decisions about program selection and adaptation
• Synthesizing and presenting data in accessible formats for a variety of audiences, including writing briefs, reports, blogs, and articles; and developing interactive data dashboard and visualizations

• Ph.D. or equivalent in economics, psychology, sociology, statistics, data science/data analytics, demography, or a related field. In lieu of a PhD, a Masters' degree with coursework in causal inference and demonstrated experience applying it to research…