ABOUT US

We are a leading global climate risk and analytics provider located in London, UK and New York, US. Earlier this year, we closed Series A led by GV (Google Ventures) and are on a fast trajectory to lead the financial services and asset management industries with our B2E SaaS platform, comprised of three products, Carta, Spectra and Adapt, which resolve customer climate risk challenges across their entire workflows.
We are at a pivotal point in scaling our product offering, expanding our market reach, and refining our product strategy to sustain continued success.

ESSENTIAL SKILLS

• Strong experience working with large geospatial raster and vector datasets (e.g. Earth Observation or climate datasets).
• Experience with processing of large and complex datasets in a python based geospatial stack (xarray, numpy, scipy, dask, geopandas, OGR, GDAL).
• Strong numerical skills with the ability to apply quantitative methods to extract meaningful insights from spatial data.
• Familiar with modelling concepts from one of hydrology, atmospheric, environmental sciences.

DESIRABLE SKILLS:

Experience in any one of these areas makes you stand out but are not required for the role and if you are passionate and interested in the role we encourage you to apply!

• Experienced with AWS, GCP, or Azure for geospatial data storage, access, and distributed computation.
• Experience processing optical or radar Earth Observation datasets.
• Experience building geospatial machine learning or physical/semi-empirical models

QUALIFICATIONS

• PhD or equivalent experience in Earth Observation, geospatial data engineering or environmental sciences.
• Minimum of 3 years industry experience in geospatial domain.

ABOUT THE ROLE

We are seeking a Scientist with extensive experience in geospatial data engineering and model development (physical, statistical or machine learning) and with expertise in Earth Observation processing techniques and sources. You will need to be adaptable to tackle technical challenges across geospatial and physical modelling domains. As a key member of the Science Team, you will be pivotal in building scalable geospatial tools, supporting hazard scientists in complex modelling tasks, creating high-quality input datasets, and future-proofing our processes to scale to new geographies and advanced methodologies.

Experience in any one of these areas makes you stand out but are not required for the role and if you are passionate and interested in the role we encourage you to apply!

• Experienced with AWS, GCP, or Azure for geospatial data storage, access, and distributed computation.
• Experience processing optical or radar Earth Observation datasets.
• Experience building geospatial machine learning or physical/semi-empirical model