Remote Sensing Specialist (Carbon Offsetting)
The Rewilding Company is a pioneering organisation dedicated to restoring and enhancing natural ecosystems through innovative rewilding practices.
Our mission is to create resilient landscapes that support biodiversity, combat climate change, and foster sustainable communities.
By leveraging cutting-edge technology and scientific research, we aim to revitalize degraded habitats and promote the reintroduction of native species.
As we expand our efforts globally, we are looking for a Remote Sensing Specialist to join our dynamic team, bringing expertise in satellite imagery and data analysis to help monitor and assess the impact of our rewilding initiatives.
Key words: Biodiversity assessments; Blue carbon; Carbon credits; Environmental Monitoring; Field Surveys; Forestry; Geospatial Analysis; LiDAR; Mangroves; REDD; Reforestation; Restoration; Sentinel 2.
Key details
Eligibility: Must have the right to work in the UKQualifications: PhD in remote sensing and a background in the private sector (candidates with an MSc in remote sensing and strong experience in the private sector will also be considered)Working Arrangement: Fully remoteSalary: £35,000 - £50,000 (dependent on experience), plus a bonus of 50% of the salary if Key Performance Indicators are me
Apologies in advance, but we won't respond to candidates that do not meet the eligibility and qualifications criteria.
Key Responsibilities
• Lead the innovation and integration of machine learning techniques to enhance the identification and classification of landcover types, ensuring high temporal and spatial resolution using remote sensing data (e.g., Sentinel 2, SAR, JAXA, Landsat imagery) while focusing on improving accuracy and reducing uncertainty. • Spearhead the development and deployment of machine learning models to monitor and predict both historic and ongoing changes in forest cover for conservation and reforestation projects, optimising outcomes through advanced analytics. • Drive the creation of dynamic, data-driven models that assess the annual risk of deforestation over the project lifetime, incorporating digital terrain models and leveraging predictive machine learning algorithms to forecast trends. • Lead carbon projection modelling over the project lifetime, utilising state-of-the-art satellite data, machine learning, and remote sensing techniques to enhance predictive accuracy. • Innovate and apply cutting-edge remote sensing and machine learning methods to monitor sea-level rise and its impact on project areas, ensuring timely insights for decision-making. • Develop models using satellite data and machine learning to determine forest height, soil organic carbon, forest biomass and tree species at high spatial and temporal resolutions, ensuring a comprehensive analysis of environmental health. • Identify suitable reforestation areas through machine learning-driven analysis of multi-source satellite and drone data, optimising land-use strategies. • Oversee the processing and analysis of drone-mounted remote sensing data, such as LiDAR, to enhance understanding of terrain and vegetation structures. • Lead efforts in modelling species zonation using advanced machine learning techniques to refine ecosystem restoration strategies. • Develop innovative methodologies for utilising remote sensing and machine learning approaches to baseline and monitor social and biodiversity impacts. • Collaborate with operational teams to integrate field data with remote sensing outputs.
Essential Skills and Qualifications:
• Master's degree or PhD in Remote Sensing, Geospatial Science, Environmental Science, or a related field, with a proven ability to lead innovation in the application of machine learning to geospatial analysis. • Extensive experience in remote sensing, GIS applications, and advanced data analytics, with a focus on leveraging machine learning to improve decision-making. • Proficiency in remote sensing software (e.g., ENVI, ERDAS Imagine) and GIS tools (e.g., ArcGIS, QGIS), as well as experience in machine learning libraries such as TensorFlow or PyTorch. • Demonstrated experience in processing and interpreting satellite imagery (e.g., Sentinel 2, Landsat) using machine learning and deep learning algorithms to reduce uncertainty and increase accuracy. • Ability to create commercial-standard data visualisations and communicate complex data insights to various audiences, from technical teams to non-expert stakeholders, adapting interpretation methods accordingly. • Willingness to work within an international, multicultural, remote team. • A commitment to openly share and collaboratively test work with colleagues throughout every stage of the process, fostering a culture of transparency, peer feedback, and continuous improvement. • Strong analytical and leadership skills, with a track record of driving innovation in remote sensing data processing and interpretation. • Ability to self-manage and adopt an agile approach to tasks, thriving in fast-paced, startup environments where adaptability and self-direction are key. • Proven commitment to staying updated with the latest advancements in remote sensing, machine learning, and environmental science, with the ability to challenge conventional approaches and foster both incremental and transformative change. • Experience incorporating fieldwork with remote sensing projects, collaborating with operational teams on the ground to collect and integrate underlying data. • Willingness to conduct field work, including to remote regions. • The right to work in the UK.
Desired Skills:
• Experience with carbon markets, Verra methodologies, and an understanding of how machine learning can optimise carbon credit calculations. • Familiarity with translating workflows into R and developing reproducible machine learning models. • Willingness to relocate to Cornwall, UK; enabling regular in person working with the CEO and Technical Lead.
What We Offer:
• £35,000 - £50,000 (dependent on experience), plus a bonus of 50% of the salary if Key Performance Indicators are met. • Flexible working hours and a supportive remote work environment. • The opportunity to lead impactful projects that contribute to climate change mitigation and biodiversity preservation. • Opportunities for professional development and growth, with a focus on driving innovation and leading advancements in remote sensing and machine learning.
How to Apply:
• Interested candidates are invited to submit: • CV, focused on outputs of each role. • A covering letter succinctly evidencing your fit to the key responsibilities, skills and qualifications. • A short description (no more than 300 words) of how you have driven innovation in a past project-particularly how you applied new technologies, improved efficiency, or solved complex problems. • Applications should be sent to with the title 'Remote Sensing Specialist Application'
