Geospatial Data Science Lead — Hazard Intelligence Architect

Neural Earth, Inc.; United States; On-site; USD 185,000 - 231,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

PTO

Life insurance (2x salary)

HSA/FSA options

Job summary

Neural Earth, Inc. seeks a founding staff-level Geospatial Data Scientist to own the data science function, define strategy for multi-hazard data ingestion, structuring, and pipelines, and empower ML/AI teams with production-ready data.

You will guide internal and external stakeholders, mentor a direct report, and set validation standards to ensure robust, scalable hazard models used by insurers, government, and infrastructure operators.

Qualifications

• Master's or PhD in Geospatial Science, Data Science, Atmospheric or Environmental Science, or a related field
• 5+ years of geospatial data experience in research or applied settings
• Experience in insurance, energy, government, defense, or climate tech
• Ability to build and lead a technical capability, including mentoring at least one direct report

Responsibilities

• Own Neural Earth's geospatial data science function and define the technical strategy for ingesting, structuring, and piping multi-hazard geospatial data
• Make data usable by ML/AI teams to power quantified intelligence products for insurers, government agencies, and infrastructure operators
• Lead as internal/external SME on geospatial data, drive customer calls, proposals, and strategy discussions, and manage a direct report
• Establish rigorous validation standards for geospatial models, ensuring reproducibility and cross-validation across geographies

Skills

Geospatial data

Python

Mentoring

Executive communication

Education

Master's or PhD in Geospatial Science / Data Science / Atmospheric or Environmental Science

Tools

GeoPandas

Rasterio

GDAL

xarray

Shapely

Job description

Neural Earth, Inc. seeks a founding staff-level Geospatial Data Scientist to own the data science function, define strategy for multi-hazard data ingestion, structuring, and pipelines, and empower ML/AI teams with production-ready data.

You will guide internal and external stakeholders, mentor a direct report, and set validation standards to ensure robust, scalable hazard models used by insurers, government, and infrastructure operators.