Job Type: Temporary, full-time, hybrid
Location: University of Washington - Seattle, WA
Salary: $78,000-$84,000/year
Application Due Date: September 29, 2025
Description:
The School of Environmental and Forest Sciences, University of Washington, is seeking a Geospatial Data Analyst with expertise in fire modeling and remotely sensed data to support innovative wildfire research that informs post-fire management decision-making. The successful candidate will contribute to a project aimed at improving our understanding of the conditions under which fuel treatments lead to desired socioecological outcomes when affected by wildfire. The analyst will collaborate with a multidisciplinary team of postdoctoral researchers, federal scientists, and university faculty to develop and refine a scalable methodology that utilizes existing spatial datasets on landscape characteristics, fire behavior, and fuel treatments. This methodology will be applied to real-world wildfire-treatment interactions across diverse U.S. landscapes to generate empirical case studies of Wildfire-Treatment Outcomes (WTOs). Fuel treatments are designed to alter fire behavior—such as reducing fire intensity, spread rate, or spotting potential—when wildfires occur. This research will provide critical scientific insight to guide future fire and land management strategies.
The selected candidate will collaborate on research evaluating recent large wildfires in the Northwest region, focusing on three key outcomes: reduction in fire severity, successful containment of fire spread, and minimized impacts to homes and critical infrastructure. These outcomes correspond to the three pillars of the National Cohesive Wildland Fire Management Strategy (“Cohesive Strategy”): fire-resilient landscapes, safe and effective wildfire response, and fire-adapted communities. The research leverages the considerable variability in landscape, fire behavior, and treatment characteristics to conduct a series of natural experiments. Using a big data and remote sensing approach, the project aims to learn from past fuel treatment activities. These empirical, data-driven assessments of Wildfire-Treatment Outcomes (WTOs) are essential for informing process-based models of future treatment effectiveness under a changing climate. They also support improved treatment design, incident decision-making, and adaptive management of fuel treatment investments. The work involves both place-based case studies and methodological development. The incumbent will collaborate with a postdoctoral scholar and partners across the country to strengthen monitoring systems, rapidly assess the drivers of treatment effectiveness in key landscapes, and support collaborative learning with agency stakeholders.
Position Complexities:
This position offers an excellent opportunity to apply diverse analytical skills in conducting original and applied research, presenting findings at scientific meetings and trainings, and contributing to peer-reviewed publications. The role also includes collaboration with research teams across the country who are conducting similar analyses as part of a larger, multi-regional project. The candidate will be expected to independently plan, prioritize, and carry out research tasks under the general guidance of a faculty member from the School of Environmental and Forest Sciences (SEFS) and a scientist from the U.S. Forest Service (USFS).
Responsibilities:
The successful candidate will collaborate with a postdoctoral scholar and other partners to help build a foundation for improving monitoring systems, rapidly assessing the drivers of treatment effectiveness across key landscapes, and fostering collaborative learning with agency stakeholders. The incumbent will be responsible for collecting, processing, and analyzing large remote sensing and spatial datasets to support research on Wildfire-Treatment Outcomes (WTOs). This work will involve the analysis of various spatial data types (e.g., vector, raster, imagery) to support multi-scale and multi-resource planning, assessment, and monitoring efforts.
Research (75%)
Incumbent will assemble, process, and analyze large remote sensing datasets (e.g., Landsat, Sentinel-2, MODIS) and spatial datasets (e.g., FACTs, FTEM, field and model-generated data) of wildfires and operational fuel treatments using machine learning, R/Python and Google Earth Engine. Selected candidate will use cartographic principles and conventions sufficient to create maps for external and internal use that display natural resource and socio-economic information and highlight analysis results. (30%)
Collaborate with a national research team—including U.S. Forest Service Research Stations and university partners—to develop and refine a core, scalable methodology that utilizes existing spatial datasets on landscape characteristics, fire behavior, and fuel treatments. This methodology will be applied to large wildfires across diverse U.S. landscapes to generate empirical case studies of Wildfire-Treatment Outcomes. (15%)
Provide research support to the team using statistical and geospatial analysis, wildland fire modeling, fuel and vegetation modeling, and risk analysis. (15%)
Co-lead the analysis of forest biometric data on post-fire stand conditions for selected recent wildfires with treated fuels in the Pacific Northwest, collaborating closely with federal, tribal, and state partners. (15%)
Project Management (15%)
Support principal investigators on project management and interface with a group of research scientists, federal cooperators, and contractors. (5%)
Write internal/technical documentation as well as to contribute to external scientific documentation, including peer-reviewed research papers. (5%)
Prepare and manage data, including collecting, cleaning, organizing, and maintaining datasets. (5%)
Outreach (10%)
Provide technical assistance and support to other projects underway within the group. (5%)
Perform related duties as required. (5%)
Minimum Qualifications:
A Bachelor’s degree in a science-related field in forestry, fire ecology, forest ecology, natural resources, or geospatial science and four years of experience integrating, processing and analyzing large remote sensing datasets (e.g., Landsat, Sentinel-2, MODIS) and spatial datasets (e.g., FACTs, FTEM, model-generated and field data) using multi-analysis, machine learning, R/Python, and Google Earth Engine for wildfire and fuel treatment outcome research.
Equivalent education/experience will substitute for all minimum qualifications except when there are legal requirements, such as a license/certification/registration.
Desired Qualifications:
Knowledge of fire science and geospatial analysis or expertise and experience using a variety of GIS, both proprietary (e.g., ArcGIS Pro, Google Earth Engine R geospatial packages, python - geopandas, arcpy) and open source (e.g., QGIS, Python, R, Julia, PostGIS, GRASS) to maintain data repositories and complete geospatial analyses.
Experience on using GIS and remote sensing to map and characterize distribution of plant species, vegetation communities, fire regimes and wildland fuels with a familiarity with FACTS, LANDFIRE, GNN, and CWHR spatial datasets.
A MS or PhD degree is desirable.
Experience on fire behavior and fire effects computer models.
Familiarity with forest and fuels inventory data and forest growth and yield modelling.
Demonstrated ability to create clear and concise visual displays of quantitative data.
Strong communication and interpersonal skills.
Ability to work collaboratively in a team environment.
