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This job is posted for Earth Finance via Parallel


Earth Finance is hiring a Senior Earth Data Scientist to contribute to the evolution of SpatiaFi, 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.


Key Responsibilities Include:


As a senior member of the team, you will sit at the intersection of climate science, software engineering, and corporate strategy and finance. You aren't just running scripts; you are the architect of our scientific logic.


  • 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, BigQuery, 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 WeatherNext2 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


Required Qualifications and Experience


We’re looking for an Earth or atmospheric scientist with a heavy-duty data science toolkit. You have 5+ years of experience in an applied science and data and modeling-intensive environment.


  • 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.


Technical Environment


  • Primary: Python (PyData stack), Google Earth Engine (GEE).
  • Cloud/AI: Google Cloud (GC), BigQuery, Vertex AI, Gemini (AI Studio, Antigravity, NotebookLM)
  • Models: WeatherNext2, Earth AI, and modern GeoAI foundational models.
  • Visualization: Competent in tools like QGIS as/when needed for rapid validation and sanity checks, but primarily focused on programmatic, scalable outputs.


Why you should join us.


Earth Finance is a dedicated team of strategists, investors, policy makers, technology developers, scientists, and subject matter experts who are committed to accelerating the global economic transition to a sustainable future. We believe climate and sustainability progress is realized when market and policy forces align. We partner with companies to reinvent the extractive business models of today, investors to deploy innovation capital at scale, governments to adopt common sense policies and incentives, community leaders to implement inclusive and equitable solutions, and the scientific community to ground our client recommendations in the natural world. We are committed to radically accelerating financially successful climate action.


Benefits:


  • Medical, dental & vision insurance. We cover 100% of your premiums and 25% of the premiums for your dependents.
  • Company funded life and disability insurance.
  • Paid vacation and 12 company holidays.
  • A 401(K) plan with company match.