At Treeconomy, we believe in the regenerative power of nature. We’re on a mission to combat climate change, restore ecosystems, and improve livelihoods. By embracing remote sensing technology and financial innovation, we are revolutionising the forest carbon market and incentivising landowners to grow flourishing, climate-positive forests. We have massive goals and are looking for passionate people to help us achieve them.
Treeconomy is a venture-backed earth-tech startup working at the intersection of technology, ecology, and finance. We were founded to close the $700B annual nature funding gap, scaling impactful nature-based solutions to realise true win-win-wins for climate, biodiversity, and human livelihoods. We harness a fusion of satellite imagery, drone data, and machine learning to bring traceability, transparency, and trust to impactful carbon-removing restoration efforts. We specialise in developing advanced digital Monitoring, Reporting, and Verification (dMRV) systems to accurately measure carbon sequestered by nature-based projects. Our work supports landowners and investors, connecting the highest-quality projects to leading corporate carbon buyers and investors via our platform.
We’re hiring a Machine Learning Scientist/statistician on a contract basis to lead the research and development for a groundbreaking R&D project, "QUBIST: Quantifying Uncertainty in Biomass using Integrated Satellite Technologies".
This project directly addresses a critical barrier in the Voluntary Carbon Market: the lack of transparent, robustly quantified uncertainty in carbon stock estimates. Your mission will be to develop a cutting-edge methodology that improves, develops and integrates our current Bayesian statistical models with multi-mission satellite data to produce more accurate and trustworthy carbon stock data.
This is a high-impact role focused on pioneering a more honest and statistically sound approach to carbon measurement. You will be responsible for the core technical research and model development, turning a scientifically ambitious concept into a pilot-tested prototype. Success in this project will not only enhance our commercial dMRV offering but also contribute valuable knowledge to the wider UK climate sector, building the trust needed to unlock significant private finance for nature.
You will report to our Science Lead, Dr. Matt Amos, and work closely with our remote sensing and product teams.
We are looking for a specialist with skills in Bayesian statistics and applied machine learning, who is excited to tackle a fundamental scientific challenge with real-world impact.
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