Job Description:
• Improve fire modeling calibration and validation and potentially develop new fuel and fire modeling approaches
• Contribute to further development of fire behavior modeling automation
• Perform spatial and temporal data analysis across large landscapes
• Develop and implement spatial data and modeling frameworks
• Develop and adhere to data standards
• Incorporate data quality assurance and quality control into all work products
• Document methodology and contribute to reports, presentations and other publications
• Contribute to proposal development for new business opportunities
Requirements:
• Experience designing and performing custom analyses of vector and raster results of inputs and outputs of a wide range of fire modeling systems
• Experience parameterizing, operating and interpreting incident-level wildfire behavior modeling systems such as FSPro, FARSITE, and their equivalents
• Proficient at using commercial or open-source GIS software (ArcMap, ArcGIS, or QGIS, for example) to perform spatial analysis of vector and raster data
• Experience writing or editing scripts to perform spatial analysis
• Able to function well as part of a multi-disciplinary problem-solving team
• Excellent attention to details
• Ability to learn new concepts quickly
• Excellent oral and written communication skills.
• Working knowledge of Rothermel-based spatial fire behavior modeling systems such as FSim, RANDIG, FConst, FSPro or FlamMap
• Working knowledge of how fire behavior modeling inputs—including surface and canopy fuel characteristics, weather/climate, and topography—affect fire behavior characteristics
• Experience producing and/or analyzing spatial fire modeling outputs, including burn probability, fire intensity, perimeter polygons, ignition sources, such as datasets hosted on the FS Research Data Archive.
• Experience with fuelscape vegetation and fuels datasets, such as datasets produced by LANDFIRE’s vegetation team and fuels team. Some examples of these datasets include: Existing Vegetation Type (EVT), Fuels Vegetation Cover (FVC), Canopy Cover (CC), Canopy Base Height (CBH), and Fuel Disturbance (FDist).
• Experience working in wildfire sciences and/or management, such as incident response, fuels management, fire research, etc.
• Experience with Python geospatial libraries such as GeoPandas, Rasterio, Shapely, GDAL, and ArcPy (or equivalent in R) with the ability to utilize existing Python scripts and to contribute to the enhancement and automation of fire modeling.
• Familiarity with land management and wildfire decision support datasets, models, software, and workflows. For example, LANDFIRE, Wildland Fire Decision Support System (WFDSS), FlamMap, Nexus, WindNinja, FSim, Interagency Fuel Treatment Decision Support System (IFTDSS), Google Earth Engine.
• Familiarity with processing remote sensing datasets such as LiDAR, NAIP, and LANDSAT.
• Understanding of ecology in dry forests and Mediterranean environments and ecological forest management; ability to effectively communicate scientific concepts to lay audiences.
• Experience supporting risk-based wildfire-incident decision making as part of a large-fire incident management team (e.g., LTAN, FBAN, etc.)
Benefits:
• Health insurance
• Unlimited PTO policy
• 401k
• Company equity
• Home office set-up allowance (one time)