Institute or Center: National Institute of Environmental Health Sciences (NIEHS)
Project Title: Harnessing Geospatial Data for Environmental Public Health Protection
Skills sought:
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Experience working with spatiotemporal data, including retrieving, integrating, and visualizing data
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Experience converting heterogeneous data to common reference frames and terminologies
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Experience conducting analysis of spatiotemporal data using open source tools
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Experience with data relevant to environmental health (e.g., census, air pollution, roadway)
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Expertise in scientific programming languages (R, Python, Java, C/C++) and Linux
About the position: NIEHS seeks a data scientist to play a critical role in helping NIEHS advance a long-term initiative focused on advancing the application of spatiotemporal data for environmental health research. The Scholar will work with partner national/international agencies and research groups to promote and adopt standards and tooling that facilitate data interoperability.
The Scholar will have the unique opportunity to apply spatiotemporal data science expertise to advance the state of the art in integrating diverse geospatial data sets for the improvement of global public health. Working as a recognized thought leader, the Scholar will have access to a network of world-class interdisciplinary scientists, gain insight into federal science and the science/policy interface, and broaden her/his skills and expertise.
About the work: The Scholar’s work will be scoped around a high-impact use case that addresses how environmental exposures and characteristics of industrialized settings impact health disparities in the aftermath of natural disasters. The Scholar will produce novel methods and tools that can be widely adapted within and beyond the environmental health and broader NIH communities, including accompanying software/code, software documentation, tutorials, manuscripts, and presentations at data science or other scientific forums. The Scholar will also deploy these tools and services in a web portal to pilot data integration methods that address the use case.
Datasets involved: De-identified patient location data will be available to support exposure analysis, along with publicly available federal datasets collected by a range of other federal agencies including:
Data types include:
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Economic: industry facility location, facility types and types of toxicants produced, urban infrastructure (sewer lines)
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Environmental: remotely sensed air quality, hazardous waste site location and materials
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Health/Demographic: census tract level indicators of social vulnerability (poverty, education, race), underlying prevalence of common health conditions
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Earth observations: elevation, inundation zones, land use/land cover
Why this project matters: The federal agencies and private companies that collect and disseminate spatiotemporal data related to environmental conditions are often doing so for purposes other than health. Consequently, these data are often difficult to incorporate in environmental health research. This project will promote research into the underlying environmental drivers of health disparities and disparate outcomes caused by natural disasters. The Scholar’s work is expected to be transformative in overcoming barriers to data interoperability and integration, ultimately leading to breakthroughs in the ability to quickly identify, understand, prevent, and respond to health threats from existing and new exposures.
Work Location: Remote; Bethesda, MD; or Research Triangle Park, NC
Work environment: The Scholar will be part of a multi-disciplinary team of data/computer scientists, statisticians, epidemiologists, exposure assessment experts, and public health professionals located at NIEHS offices in Research Triangle Park, NC, and Bethesda, MD. The Scholar will also interact with external experts in geospatial data science and environmental health research—including at NOAA, NASA, and CDC—and NIH intramural and extramural researchers using data. The Scholar will directly work with staff in the NIEHS Office of Data Science and Office of Scientific Computing in developing and deploying tools and services.
To apply to this or other DATA Scholar positions, please see instructions here: datascience.nih.gov/data-scholars-2021.
