UK remote
Senior Technical Advisor, Data Use and Decision Science (Remote - US, UK, France, DRC)
About this role
Resolve to Save Lives (RTSL) is a global health organization that partners locally and globally to create and scale solutions to the world’s deadliest health threats. Millions of people die from preventable health threats. We collaborate to close the gap between proven, life-saving solutions and the people who need them. Since 2017, we’ve worked with governments and other partners in more than 60 countries to save millions of lives.
We work toward a future where people live longer, healthier lives, communities flourish, and economies thrive. This is an ambitious vision, and it inspires us and our partners to make progress every day. Resolve to Save Lives' Prevent Epidemics program works closely with Ministries of Health and national public health institutes in Africa to strengthen epidemic preparedness capacity, accelerate disease detection and response, and effectively use data to inform action.
In addition to direct partnerships with governments, we also work with implementing partners, including the US CDC, the World Health Organization, the World Bank, and the Global Health Advocacy Incubator, to implement programs, prototype innovations, and ensure sustainability through increased national ownership, including domestic budget allocations. Position Purpose: The Senior Technical Advisor, Data Use and Decision Science focuses on bridging the gap between complex epidemiological and contextual data and strategic public health action.
This role will primarily support the Democratic Republic of the Congo (DRC) Collaborative Surveillance (CS) portfolio, with a significant focus on accompaniment of DRC-based staff on 1) advancing data use for the current Ebola outbreak preparedness and rapid response, and 2) generating actionable analytics and information products to strengthen real-time decision-making, integrating disease surveillance with contextual data (e.g., climate, environmental, risk, and vulnerability factors) and supporting resilient data pipelines.