Data Scientist, Remote Sensing & AI | Life on Marsh Hire post-funding | Fractional now, Full-time post-funding

Wetland restoration is becoming a regulated, investable asset class. Life on Marsh is building the MRV infrastructure that makes it measurable, auditable, and financeable. We combine IoT sensors, satellite remote sensing, and AI to produce the water and biodiversity data that restoration companies, corporates, and regulators actually need.

This is a new company stemming from the NGO Mission to Marsh (1M€ in corporate partnerships, 230k sqm of wetland restored, partners like Patagonia & Tomorrow Bank).

We have a paid pilot, LOIs in hand, and a pre-seed raise underway. We’d like you in a fractional advisor role now. As soon as funding closes, we want you ready to join our CTO full-time as our first employee and build the AI models that will make wetland water MRV faster, cheaper, and more scalable than anything on the market.

What you will do:

• Build and train AI models using remote sensing data, satellite imagery, and drone outputs
• Work with our CTO to develop a scalable, cost-efficient water MRV for wetland projects
• Integrate multi-source data: IoT sensors, satellite, drone, and field measurements
• Apply spatial data science techniques to analyse and interpret geospatial datasets at scale
• Help make Life on Marsh the most efficient and affordable MRV platform for water in wetland restoration

What we are looking for:

• 3+ years in a commercial company working with remote sensing, AI, and/or machine learning
• Hands-on project experience building AI models with remote sensing data
• Proficiency in spatial data science: geospatial analysis, vector/raster data processing, and tools such as GDAL, GeoPandas, or similar
• Bonus: experience with MRV, wetlands or nature-based solutions

Compensation: Now: Fractional with Equity-based now. Post-funding: First employee with full-time salary (based on your experience), working directly with the CTO to build the technical backbone of the company.

Interested? Send us an email [email protected] with your Linkedin profile and an answer to this question: why should we choose you?