Geospatial Statistician for Extreme Weather and Model Evaluation (Academic Hourly)

• Conduct retrospective analyses of extreme weather events, including storms, high winds, heatwaves, extreme rainfall, and flooding.

• Perform quantitative comparisons between simulated forecasts and observational datasets across space and time.
• Develop statistical metrics and geospatial methods to evaluate model performance, bias, uncertainty, and event detection skill.
• Process and analyze large-scale gridded and point-based environmental datasets, including NWP outputs, station observations, radar, satellite, and reanalysis products.
• Support spatial matching, interpolation, aggregation, and validation workflows for model-observation comparison.
• Collaborate with software engineers and domain experts to develop automated workflows for data ingestion, preprocessing, post-processing, visualization, and analysis. Contribute to the development of operational evaluation tools and decision-support products for extreme weather applications.

Do you have experience in Statistics research?, The DPI Climate Hub (https://www.climate-dpi.org) is seeking a highly motivated candidate with expertise in statistics, geospatial data analysis, environmental data science, atmospheric sciences, remote sensing, or a related field to support research and development for the AerisIQ weather forecasting system (https://aerisiq.io) The ideal candidate will contribute as an Extreme Weather and Geospatial Statistics Analyst, supporting the analysis of high-impact weather events and the evaluation of model performance against observational datasets. This role will focus on developing statistical and geospatial workflows to compare numerical weather model outputs with in-situ observations, radar, satellite, reanalysis, and other environmental datasets. The candidate will help translate model-observation insights into operational tools, performance metrics, and decision-support products., * Background in statistics, geospatial data science, atmospheric sciences, environmental data science, remote sensing, engineering, or a related field.

• Experience working with large-scale environmental, weather, climate, or geospatial datasets.
• Strong programming skills in Python, R, MATLAB, or similar languages.
• Familiarity with statistical modeling, spatial analysis, uncertainty analysis, or model evaluation methods.

KNOWLEDGE SKILLS AND ABILITIES

• Experience working with gridded model outputs and point-based observational datasets.
• Working knowledge of environmental and weather data sources, such as in-situ observations, radar, satellite products, reanalysis datasets, or numerical weather prediction model outputs.
• Familiarity with geospatial data tools, packages, and formats, such as NetCDF, GeoTIFF, shapefiles, GeoJSON, xarray, rasterio, geopandas, ArcGIS, or QGIS.
• Experience with spatial matching, interpolation, aggregation, bias correction, validation, or uncertainty quantification across model and observation datasets.
• Understanding of atmospheric processes, weather systems, or extreme weather events.
• Experience with automated workflows, data visualization, dashboards, or operational forecasting systems.

4.24.2 out of 5 stars Urbana, IL $20 - $25 an hour - Part-time, This is a part-time Academic Hourly position. The expected start date is as soon as possible after the search posting closes. The hourly range for this position is $20-$25 per hour based on experience.