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 three Researchers on the Simulation Science team. This position is focused on simulation modeling to quantify the potential costs and effects of health interventions designed to reduce disease burden globally. The objectives of the project are to develop modular components for our Python-based microsimulation framework and to validate/verify their behavior independently and in combination. This will require conducting background research into health interventions, risk factors, and diseases to understand current knowledge about their impacts and pathways and how this knowledge corresponds to GBD concepts; working with software engineers who will be programming in Python to implement and test modular components necessary for simulation models; and communicating results to the Simulation Science team lead.
We are looking for someone who has: experience with simulation modeling, the ability to develop new methods and tools, and familiarity with epidemiology, statistics, disease modeling, data science, or related interests. 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 the intellectual exchange about how to improve upon and disseminate the results. You thrive in a collaborative work environment and are capable of working on multiple projects concurrently while meeting deadlines. Overall, you will be joining and agile and collaborative team environment.
- Develop a core understanding of assigned research area methodology and its components.
- Oversee research efforts for specific health intervention models.
- Review and assess scientific literature and available data sources in order to determine their relevance and utility for ongoing analyses.
- Under the guidance of experienced scientist and/or faculty, carry out quantitative analyses and participate in reciprocal research projects utilizing microsimulation framework, Vivarium. Interpret and vet results from junior staff, formulate conclusions and inform team leaders.
- Drive new methods to analyze structured and unstructured data; introduces novel diagnostics and presentation paradigms to communicate results.
- Model causes of death, non¬fatal health outcomes, and risk factor exposures using data from a multitude of sources and applying complex algorithms and statistical applications.
- Communicate methods and results to different audiences, helping them to learn and apply new skills.
- Contribute to creation of presentations, manuscripts, and funding proposals. Co-author papers.
- Maintain scientific awareness and intellectual agility with data, methods, and analytic techniques.
- 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, SAS).
- 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
- 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.
Conditions of Employment: Weekend and evening work sometimes required.
Please Note: Priority consideration will be given to applications received on or before May 19th, 2019.