Research Scientist (Temporary), RGUD

University of Washington•Washington, DC

1d•$110,052 - $137,460•Remote

About The Position

The Institute for Health Metrics and Evaluation (IHME) has an outstanding opportunity for a Research Scientist to join their Reproductive, Genitourinary and Digestive (RGUD) disease estimation team. Reporting to the Acting Assistant Professor, the Research Scientist is responsible for driving high-impact global health research at IHME, analyzing quantitative data across causes of death, epidemiology, and social determinants to produce key estimates that inform global health policy. This role involves leading research design, mentoring junior staff, and contributing to peer-reviewed publications while managing multiple projects in a collaborative environment. The ideal candidate is a motivated, deadline-driven researcher ready to advance their career by translating cutting-edge scientific advances into meaningful global health insights. Our office is located in Seattle, Washington at the Hans Rosling Center for Population Health at the University of Washington. This position is eligible to work fully remote in the US; work schedule required to overlap 50% of IHME office hours, between 8 a.m. and 6 p.m. Pacific Time as agreed upon between employee and supervisor.

Requirements

Master’s degree in public health, epidemiology, statistics, biostatistics, math, economics, quantitative social sciences, or related discipline plus four years' related experience.
Growing peer network where sought out as having solid command of engineering/technical areas, a given disease, risk, key indicator, relevant methodological area, and the related data sources and scientific underpinnings.
Excellent analytic, critical thinking, and quantitative skills.
Results- and detail-oriented individual who 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.
Experience devising and executing statistical modeling techniques.
Demonstrated ability to quickly recognize problems in results and identify root causes in data, methods, and code.
Ease in designing, executing, and troubleshooting code in R.
Excellent written and oral communication skills required, including track record of success in co-authorship on multiple scientific papers, presenting results, and representing research at meetings.
Demonstrated ability to contribute to and sustain collaborations with external research teams.
Experience in working with large data sets, including devising strategies for reliable data management for large data sets, computationally efficient modeling techniques to use such data sets, and critical evaluation of highly multidimensional model outputs that result from such work.
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.

Nice To Haves

MD or PhD in public health, epidemiology, statistics, biostatistics, math, economics, or quantitative social sciences plus two years’ experience preferred.
Experience with hepatic or other digestive disease epidemiology strongly desired.
Experience with endocrine, metabolic, or genitourinary disease epidemiology also somewhat desirable.
Expertise in implementing statistical models in a cluster environment to conduct analyses of large datasets and produce large numbers of estimates.
Experience applying rigorous approaches to model selection, testing, vetting and visualization.
Experience with machine learning, data mining, and analytic techniques.
Experience mentoring and developing junior employees on soft and technical skills.
Experience with project management methods.
Peer-reviewed publication record.

Responsibilities

Exhibit command of one or more of the research areas in the RGUD portfolio, including the methodology and its components.
Independently carry out quantitative analyses and participate in reciprocal research projects.
Interpret and vet results from junior staff, formulate conclusions, and inform team leaders. (45%)
Develop and implement new computational and statistical methods.
Create, test, and use relevant computer code in R.
Maintain, modify, and execute analytic machinery that generates results.
Develop, quality check, and distribute complex datasets to be used in epidemiological and statistical analyses. (20%)
Assist in drafting presentations and manuscripts and contribute to funding proposals.
Lead and co-author scientific articles in peer-reviewed journals. (15%)
May lead and/or mentor junior staff.
Provide ideas and content for the development of internal trainings.
Teach established trainings.
Contribute to research design. (10%)
Maintain scientific awareness and intellectual agility with data, methods, and analytic techniques. (5%)
Other duties as assigned that fall within reasonable scope of research team. (5%)