The Institute for Health Metrics and Evaluation (IHME) is an independent research center at the University of Washington. Its mission is to monitor global health conditions and health systems, as well as to evaluate interventions, initiatives, and reforms. IHME carries out a range of projects within different research areas including: the Global Burden of Diseases, Injuries, and Risk Factors; Future Health Scenarios; Costs and Cost Effectiveness; Local Burden of Disease; Resource Tracking; and Impact Evaluations. The aim is to provide policymakers, donors, and researchers with the highest-quality quantitative evidence base to make decisions that achieve better health.
IHME has an excellent opportunity for a Researcher to join our Causes of Death, Shocks, and Intermediate Causes team. IHME researchers analyze and produce key estimates for their assigned research team and will assess all available quantitative data – including those on causes of death, epidemiology, and a range of determinants such as education and income – from surveys, vital registration, censuses, literature, registries, and administrative records. Using established modeling tools and through creation of novel code, researchers incorporate all relevant data to produce the most up-to-date and scientifically credible results.
You will be integrally involved in producing, critiquing, improving, and disseminating results. You already have a command of epidemiology, statistics, disease modeling, or related interests and we will help you develop an understanding of our core research and methodology. Our researchers work with senior research leads and external collaborators and take part in intellectual exchange about how to improve upon and disseminate the results.
You are expected to interact successfully with a wide range of partners and to describe complex concepts and materials concisely. Overall, Researchers are critical members of agile, dynamic research teams. This position is contingent on project funding availability.
- Develop a core understanding of multiple cause of death data and modeling, methodology and its components. Additionally, this researcher will develop a core understanding of antimicrobial resistance in the areas of data sources, processing, and modeling.
- Under the guidance of experienced scientists and/or faculty, carry out quantitative analyses and statistical modeling for both fatal and nonfatal estimates to produce results designated on a given timeline as part of collaborative research projects.
- Extract data from various sources and databases. Format, transform, review, and assess data sources to determine their relevance and utility for ongoing analysis. Understand key data sources and variations in these across and within countries.
- Review, assess, and improve results and methods.
- Apply computational and statistical tools and algorithms for the preprocessing, analysis, and visualization of source data.
- Document code and analytic approaches systematically so that analyses can be replicated by other team members.
- Lead discussion in research meetings about results and analyses to vet, improve, and finalize results.
- Contribute to creation of presentations, manuscripts, and funding proposals. Co-author paper(s).
- Maintain scientific awareness and intellectual agility with data, methods, and analytic techniques.
- Complete other duties as assigned that fall within reasonable scope of research team.
Master’s degree in public health, epidemiology, statistics, biostatistics, math, economics, quantitative social sciences, or related discipline plus 1 year related experience or equivalent combination of education and experience.
- Proven interest and some experience in a given disease, risk, key indicator, methodological area, and the related data sources and scientific underpinnings.
- Strong analytic, critical thinking, and quantitative skills.
- Ability to professionally and effectively communicate and work with other staff at all levels in order to achieve team goals for the analyses and related outputs.
- Results- and detail-oriented individual that can initiate and complete tasks under tight deadlines and changing priorities both independently and in a team environment. Flexibility with hours and workload is key.
- Working ability with at least one statistical programming language (e.g. R, Python, Stata).
- Familiarity with simple and logistic regression.
- Excellent communication skills, both oral and written.
- Ability to work both independently and in collaboration with a team.
- A long-term interest in a research scientist position contributing to the overall mission of our research.
Equivalent education/experience will substitute for all minimum qualifications except when there are legal requirements, such as a license/certification/registration.
- Experience in applied mathematics, including statistical modeling methods.
- Track record of success in co-authorship on scientific papers, presenting results, and representing research at meetings.
- Knowledge of machine learning, data mining, and analytic techniques.
- Familiarity with SQL.
Conditions of Employment: Weekend and evening work sometimes required.