Description:

Our client is a global leader in climate catastrophe risk modeling, providing advanced solutions to insurers, governments, and financial institutions.
They offer models covering over 190 countries, delivering high-resolution risk assessments for inland flooding, storm surges, tropical cyclone wind, severe convective storms, and wildfires.
The company utilizes state-of-the-art probabilistic modeling, which is known for its speed, technical excellence, and transparency.
As a pioneer in integrating climate change scenarios, their product helps clients enhance resilience and financial sustainability in an increasingly volatile environment.
Their proprietary financial model enables smarter underwriting, investment, and mitigation strategies.
The company is committed to transparency, innovation, and customer-centricity, aiming to shape a resilient future for generations to come.

Requirements:

Candidates must have 5+ years of experience in GIS data science, spatial analysis, or geospatial modeling.
A strong proficiency in GIS software such as QGIS, ArcGIS, or GDAL is required.
Expertise in Python and/or R for geospatial analysis is necessary, including experience with libraries such as GeoPandas, Rasterio, Shapely, and Fiona.
Knowledge of spatial data formats like GeoJSON, shapefiles, and NetCDF, along with best practices for handling large geospatial datasets, is essential.
Experience with spatial databases such as PostGIS and distributed computing for geospatial analysis is required.
Excellent communication skills and the ability to work in a collaborative, fast-paced environment are mandatory.

The position offers flexible work arrangements to accommodate various lifestyles.
Employees will enjoy a collaborative and supportive team culture.
A truly competitive salary is provided to attract top talent.
Help and support from a caring HR team are available to assist employees.