Senior Earth Data Scientist

Overview

Earth Finance is hiring a Senior Earth Data Scientist to contribute to the evolution of Spatia Fi, our award winning geospatial platform, and our emerging energy, water and nature products. You will own the "Science-to-Insight" pipeline, leveraging traditional data and tooling, as well as the emerging field of GeoAI, and foundational planetary models to work alongside our Spatial Finance team that turns complex earth observations into derivative financial models that answer the toughest financial questions amidst a changing planet.

• Scientific Ownership: Lead the development of derivative geospatial insights. You will move beyond foundational data (NDVI, temperature, precipitation) to create sophisticated geospatial models that inform and understand financial impact, supply chain resilience, and energy transition risks.
• Applied Science Delivery: Lead product-driven R&D by architecting production-ready models that translate peer-reviewed scientific formulas into derivative planetary and geospatial indices, harmonizing full-spectrum data streams ranging from observed satellite rasters to reanalysis grids and multi-decadal climate projections into unified analytical layers that advance our core spatial reasoning capabilities.
• Modern Remote Sensing: Leverage and work in cloud-native tools like Google Earth Engine, Big Query, and STAC, with strong python coding experience and development best practices.
• GeoAI & Foundational Models: Deploy and fine-tune state-of-the-art models, including Google’s Weather
  
  Next2 and other foundational Earth AI models, to perform multi-layer spatial reasoning that moves past simple pattern recognition to derivative, complex insights.
• The "Agentic" Vision: Help us build the next generation of spatial reasoning agents. You will work to solve the "2025 benchmark" problem: ensuring AI can reason across five overlapping datasets (e.g., energy risk + zoning + climate risk + policy exposure) to provide a decision a customer can make executive-level strategic and financial decisions.
• Cross-Functional Collaboration: Partner with our Spatial Finance analysts to go beyond risk reporting and compliance, translating environmental signals into financial impacts and strategic decision-making.
• Collaborate with the technology team to ensure models are production-ready and optimized for cloud infrastructure.
• Engage with external academic & strategic advisors to ensure our methodology remains peer-review caliber while being commercially disruptive.
• Mentor other team members and provide thought leadership for our external scientific relationships

• Scientific Knowledge: You possess a rigorous background in Earth Sciences and demonstrated experience in developing applied remote-sensing methodologies for derivative analytics using multi-source datasets and scientific formulas, with a focus on product-driven R&D to evaluate and integrate new models and techniques that directly enhance our spatial analysis capabilities.
• Applied R&D Frameworks: Proficiency in translating peer-reviewed formulas and scientific literature into production-ready Python code for derivative planetary and geospatial indices.
• Multi-Source Integration: Expertise in harmonizing disparate "full-spectrum" data streams, bridging the gap between observed rasters, reanalysis grids, and multi-decadal projection arrays, to create unified analytical layers.
• Advanced Python: You are expert-level in the PyData stack: pandas, geopandas, Xarray, Dask, and scikit-learn.
• Cloud-Native Geospatial: You have deep experience with Google Earth Engine, Vertex AI, or similar cloud-scale geospatial platforms. Competent in tools like QGIS as/when needed.
• Reasoning Over Retrieval: You understand that the hard part isn't finding data, it's the multi-layer reasoning required to turn "it's raining" into "here is the projected crop yield financial loss for this specific portfolio."
• Curious & Agentic: You are excited by the convergence of AI and geography. You want to build systems that don't just "show a map" but "recommend action”, rooted in defensible and observable Earth sciences methodologies.