Senior Environmental Data Scientist

Role Description

Job Duties And Responsibilities

• Lead Development of Data Solutions: Design and implement advanced data pipelines, metrics, and models that assess how businesses interface with nature.
• Apply Environmental Science at Scale: Translate robust environmental science into analytical workflows that can support business decisions and regulatory needs.
• Drive Methodological Rigor: Incorporate peer-reviewed methodologies and scientific best practices into product development; stay ahead of innovations in the field.
• Architect Scalable Data Solutions: Develop performant, production-ready code and collaborate with engineers to build tools for spatial, temporal, and exploratory analysis.
• Mentor and Guide: Support junior data scientists, serve as the go-to environmental expert across functions, and help build the team’s overall environmental data science capacity.
• Engage with Frameworks: Apply knowledge of sustainability disclosure and risk frameworks (e.g. TNFD, ESRS, SBTN, SFDR) to develop solutions that meet evolving stakeholder needs.
• Collaborate and Communicate: Work cross-functionally with product, research, and engineering teams to translate scientific insight into real-world impact. Represent your work with external stakeholders as needed.
  

Requirements Experience, Qualifications And Skills

• Environmental Expertise: PhD (preferred) or Master’s in environmental science, ecology, conservation, geosciences, or a closely related field.
• Experience: 5+ years applying data science to environmental or sustainability contexts; experience in a product-oriented or startup environment is a must.
• Programming & Engineering: Expert Python developer with strong engineering discipline (e.g., Git, unit testing, CI/CD); experience building high-quality analytical code.
• Geospatial & Remote Sensing: Advanced skills in spatial analysis, GIS tools, and remote sensing data workflows (e.g., raster/vector processing, spatial joins, indexing).
• Data Science & Machine Learning: Proficiency in statistical modelling, spatial ML, and fundamental AI/ML methods (e.g., scikit-learn, PyTorch, foundation models).
• Data Systems: Hands-on experience with relational and spatial databases (e.g., PostGIS), cloud data tools (e.g., Snowflake), and handling unstructured and structured data.
• Framework Fluency: Demonstrated ability to interpret and implement solutions aligned with environmental frameworks such as TNFD, ESRS, SFDR, and SBTN.
• Communication: Ability to explain complex ideas clearly to both technical and non-technical audiences; experience with data storytelling and visualization is a plus.
• Team Fit: Collaborative, proactive, impact-driven, and adaptable — comfortable with the fast pace and opportunities of a growing startup.
  

Preferred Qualifications

• Deep experience with one or more particular nature-related domains, such as: biodiversity impact modeling; physical risk analysis; nature risk valuation.
• Experience contributing to or leading cross-disciplinary scientific or open-source projects.
• Work experience at corporate sustainability offices, financial institutions, regulatory bodies, or nature data providers.