Location: Durham, NC
Cap-Exempt H-1B Position — No lottery required
Why RTI RTI International is an independent, scientific research institute dedicated to improving the human condition. Our vision is to address the world's most critical problems with technical and science-based solutions in pursuit of a better future. Clients rely on us to answer questions that demand an objective and multidisciplinary approach—one that integrates expertise across social, statistical, data, and laboratory sciences, engineering, and other technical disciplines to solve the world’s most challenging problems. We believe in the promise of science and technical solutions, and we push ourselves every day to deliver on that promise for the good of people, communities, and businesses in the US and around the world. If you are looking for the opportunity to make a real difference, RTI is the place for you. About the Hiring Group RTI International’s Health Care Systems Research Department has multiple openings for ***Bachelor's-level Health Economists***to assist with implementation, analysis, evaluation, and synthesis of innovative health and social policies, programs, and research initiatives. In this position, you will function as an integral part of a team supporting research and analytic activities. The role includes opportunities to apply quantitative skills and collaborate with senior research staff, while developing an in-depth understanding of critical health policy and research issues, and advanced methods. Many opportunities for mentorship and professional development in a variety of health-systems-related topic areas are available. **This position can be either based in RTI’s U.S. office in Research Triangle Park, NC****, or it can be fully remote.** **Teleworking is permitted within the United States** **only****.** **We strongly encourage applicants to include a cover letter with their application describing their interest in the role and related experience.** What You'll Do - Assist with quantitative research activities including data abstract