Geospatial Data Scientist

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Neural Earth

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

🛡️ Insurance

🏠 Real Estate

Artificial Intelligence • Insurance • Real Estate

Neural Earth is an AI-driven geospatial intelligence company that provides a platform (Prometheus) and APIs to deliver property-level physical risk analytics for property & casualty insurers and commercial real estate/asset managers. Its product suite includes Compass Asset Finder (search across 150M+ U. S. parcels), Fulcrum catastrophe modeling, Pyre wildfire intelligence, Roof Analytics API, Parcel Enrichment API (350+ underwriting-grade fields), and other tools that fuse satellite imagery, IoT, climate models and economic data into underwriting and portfolio decisioning. The platform emphasizes faster diligence, loss compression, and decision velocity by surfacing address-level risk signals and integrated AI engines for underwriting and asset management workflows.

📋 Description

• Design and deploy Python-based numerical weather prediction and geospatial models that quantify atmospheric hazards at a level of precision that drives real business decisions, not just research outputs. • Architect Neural Earth's atmospheric analytics capability from the ground up, including the team, data pipelines, and scientific frameworks that turn hazard data into indexed intelligence products customers can act on. • Convert complex, multi-hazard scientific findings into business-ready products that are legible and compelling to insurance underwriters, government operators, and enterprise decision-makers. • Serve as Neural Earth's internal and external subject matter expert on weather and climate, driving customer calls, proposals, and strategy discussions. • Establish rigorous validation standards for all atmospheric models, ensuring methods are reproducible, cross-validated, and defensible across industries and geographies.

🎯 Requirements

• 3 or more years working with atmospheric and geospatial data, including numerical weather prediction models (WRF, ECMWF) and multi-hazard analysis at scale. • You write Python fluently and have built production-grade geospatial models using GeoPandas, Rasterio, GDAL, xarray, and Shapely. • You have applied ML to real atmospheric or climate problems using PyTorch, TensorFlow, or scikit-learn and shipped the results. • You are fluent in geospatial data formats (GeoTIFF, COG, GeoParquet, NetCDF) and have worked with large-scale raster, vector, and time-series datasets in cloud environments. • You have shaped a scientific framework or research agenda, not just executed within someone else's. • You think in systems. When you see a wildfire burn scar, your mind goes to downstream snowpack risk. • You can walk a customer through a confidence interval in the morning and brief a C-suite on business implications in the afternoon. • You define your own structure in ambiguous environments and build something real before the roadmap is written. • You do not ship unvalidated models. You have the judgment to know when something is ready. • You care when your hazard model shapes an insurance decision or informs a disaster response—this matters to you.

🏖️ Benefits

• Competitive base compensation regardless of work location • Company performance-based cash bonuses • Majority employer-paid health, dental, and vision insurance for you and your dependents • Flexible Paid Time Off (PTO) • Group Life Insurance at 2x your base salary, paid by the company • FSA and HSA options to maximize your healthcare dollars