Geospatial Data Scientist

2h2 hours ago

Mercury Insurance

US · Full-time · $86,110 – $199,452

About this role

We are searching for a creative and analytical Geospatial Data Scientist to join our collaborative data science team. In this role, you will work with diverse and complex geospatial datasets to solve pressing challenges in the [re]insurance industry. You will collect, analyze, and interpret intricate geospatial data to inform critical business decisions.

Develop and implement sophisticated risk models and climate analytics while optimizing operational strategies through data-driven insights. Architect and automate robust, scalable geospatial data pipelines using Python and libraries like GeoPandas and Rasterio. Ingest, clean, and validate diverse data types from vector and raster to point cloud and H3 indexes.

Apply advanced spatial statistical methods including spatial clustering and spatiotemporal mining to uncover hidden patterns. Partner closely with data scientists, engineers, and business leaders to define data requirements. Strategically expand the central geospatial data library in a dynamic, supportive environment.

Create compelling interactive visualizations and dashboards with tools like Hex, Kepler.gl, or ArcGIS to communicate insights. Hands-on experience with GIS platforms, geospatial data pipelines, and machine learning is essential. A background in catastrophe loss estimation ensures success with real-world impact.

Requirements

Bachelor’s or Master’s degree in Geography, Computer Science, Data Science, Environmental Science, or a related field
3+ years of experience working with machine learning, geospatial data and GIS tools like ArcGIS Pro, QGIS, Carto
Proficiency in Python with experience using geospatial libraries like GeoPandas, Shapely, Rasterio
Experience working in the insurance or reinsurance industry, particularly exposure modeling, catastrophe risk, or underwriting analytics
Familiarity with catastrophe modeling platforms like Verisk Touchstone, Moody’s RMS
Understanding of catastrophe risk concepts and metrics such as Hazard
Hands-on experience with GIS platforms and proven ability to build and maintain geospatial data pipelines

Responsibilities

Design, build, and maintain robust, scalable geospatial data pipelines using Python and open-source libraries like GeoPandas and Rasterio
Ingest, clean, and validate diverse geospatial data types including vector, raster, point cloud, and tiled indexes like H3
Apply advanced spatial statistical methods including spatial clustering and spatiotemporal mining to uncover patterns and trends
Partner closely with data scientists, engineers, and business leaders to understand challenges and expand the geospatial data library
Create compelling interactive visualizations and dashboards using tools like Hex, Kepler.gl, or ArcGIS to communicate insights
Develop and implement sophisticated risk models and climate analytics
Collect, analyze, and interpret intricate geospatial data to inform critical business decisions