Senior Geospatial Data Scientist

Zulu Ecosystems; London; On-site; GBP 35,000 - 55,000; Full time

Job summary

A leading company focused on environmental restoration is looking for a physical, environmental, or data scientist. This role involves deriving insights from complex data to support nature-based solutions, collaborating with R&D and engineering teams, and applying scientific methodologies to address challenges in biodiversity and climate change.

Qualifications

• Experience in Python for data insights.
• Ability to analyze large geospatial datasets.
• Collaborative team approach and interdisciplinary engagement.

Responsibilities

• Develop data-driven insights and assess feasibility for nature-based solutions.
• Perform exploratory data analysis to identify trends and patterns.
• Document methods and communicate complex ideas effectively.

Skills

Data analysis

Python

Data science techniques

Statistical analysis

Geospatial analysis

Education

Degree in a numerate subject

Job description

Who are we?

Zulu Ecosystems' mission is to regenerate natural ecosystems responsibly so that the planet and its communities can thrive. We combine proprietary technology and domain expertise with on-the-ground relationships to mobilise landowners, investors, government bodies, and communities around large-scale nature programmes.

Doing the right thing for nature and people is at the heart of our work, and we apply peer-reviewed and novel scientific methods to scope, plan, and support the delivery of on-the-ground regeneration projects. We use high-quality science to maximise benefits across carbon sequestration, biodiversity uplift, nature-based climate risk solutions, and positive outcomes for communities. We have delivered projects in woodland creation and peatland restoration, and are expanding to wetlands, grasslands, and other ecosystems.

Your mission

We're seeking an innovative and collaborative physical, environmental, or data scientist passionate about extracting new scientific insights from big, complex, or noisy data. Our goal is to combine UK-wide and eventually global data and model outputs to assess landscape potential for restoration, improve outcomes for nature, and implement nature-based solutions to grand challenges such as climate change and biodiversity loss.

You will work closely with subject matter experts in R&D, Engineering, and Operations teams to conduct research and develop innovative solutions for environmental restoration. Collaborating with scientists and engineers, you'll implement and automate technical solutions, leveraging big geospatial datasets and cutting-edge data science techniques. You will also engage with field practitioners through site visits to understand restoration realities and optimize practices.

Responsibilities

• Develop data-driven and science-based insights on nature and nature-based solutions, including methodology research, feasibility assessment, training/testing approaches, and model deployment.
• Measure and communicate data and insight quality.
• Deploy models into our natural capital assessment tool, Prospector, integrating various datasets and models to derive new insights.
• Perform exploratory data analysis using statistical and visualization tools to identify patterns, trends, and anomalies.
• Clean, preprocess, and standardize diverse observational and model data from satellite, maps, drone, and in-situ sources.
• Understand scientific and business contexts to build effective solutions iteratively and in an Agile manner.
• Create scalable solutions adaptable to different locations and datasets.
• Document methods and pursue continuous improvement.
• Communicate complex ideas effectively to various audiences.
• Collaborate within interdisciplinary and cross-functional teams.

Your profile

• Passion for positive impact on nature and communities.
• Collaborative team approach and interdisciplinary engagement.
• Experience understanding user requirements and iterative development.
• Significant Python experience for deriving insights.
• Experience applying data science techniques to analyze large geospatial and non-geospatial datasets to solve real-world problems.
• Ability to present and document technical ideas clearly for diverse audiences.
• Scientific and methodical problem-solving approach focused on relevant outcomes.
• Degree in a numerate subject (e.g., physics, environmental science, engineering) or equivalent experience.