What You'll Do + Design and implement applied statistical analyses to address population-level questions related to demography, socioeconomic dynamics, and public health outcomes + Develop and apply forecasting and time-series models to inform planning, scenario analysis, and strategic decision-making + Analyze complex, real-world datasets (e.g., surveys, surveillance systems, administrative and programmatic data), often characterized by missingness, bias, or measurement limitations + Apply advanced modeling methods to extrapolate evidence on program/ intervention effectiveness to different contexts and populations, generating rigorous projections to inform program scale-up and future investment planning + Quantify and communicate assumptions, uncertainty, and limitations of analyses to both technical and non-technical audiences + Collaborate closely with interdisciplinary teams to co-develop research questions and analytical approaches + Translate statistical results into clear, actionable insights for internal stakeholders and external partners + Contribute to high-quality applied research outputs, including internal reports, policy briefs, and peer-reviewed publications + Support reproducible research practices through well-documented, maintainable code and analytical workflows + Some international travel may be required Your Experience + PhD in statistics, biostatistics or related quantitative discipline (e.g., mathematical demography, economics, data science, etc.) + Minimum of five (5) years of post-PhD experience conducting applied statistical research in public health or population-level research settings + Demonstrated experience applying statistical methods to demographic, socioeconomic, and health-related research questions + Strong expertise in forecasting and time-series analysis, including model validation, uncertainty quantification and scenario-based analysis + Advanced skills in Python and R, with experience developing reproducible and scalable analytical workflows + Proven ability to work with messy, incomplete, and imperfect real-world data + Experience collaborating in interdisciplinary and cross-sector research environments Other Attributes + Strong written and verbal communication skills, with the ability to explain statistical findings clearly to diverse audiences + Experience working in global health and low- and middle-income countries is a plus ** Must have unrestricted work authorization in the country where this position is located. The IDM team is composed of research scientists and software developers who create advanced models of disease transmission, develop computational tools to inform global disease eradication policy, conduct analysis of epidemiologically- and policy-relevant data, and identify and address critical knowledge gaps.