Spatial Analyst (Remote - UK only)

Nature-based Insights
Bury
2 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Scientist

Head of Data Science

Bioinformatician

Applied Machine Learning Researcher (we have office locations in Cambridge, Leeds & London)

Data Scientist

Job Title: Spatial Analyst

Contract:Permanent, full time

Salary:£32,000

Location: UK-based. Remote with provision for co-working space/office.


We are seeking a consultant with expertise in spatial analysis and a background in biodiversity to join the Nature-based Insights research team. 


This is an exciting opportunity to be part of an ambitious mission to translate cutting-edge science into practice. We work with a range of clients on how to tackle the most critical issues of our time - climate change and biodiversity loss -, whilst supporting economic recovery. We are looking for a talented individual with skills in data analysis and spatial analysis to help us work with more clients across a broad range of supply chains and landscapes.


Who we are

Nature-based Insights is a Social Venture spin-out of the University of Oxford’s Nature-based Solutions Initiative. Our mission is to apply the very latest science to help businesses implement nature-based solutions with integrity. 


Drawing on the University’s world-leading expertise and network, we apply cutting-edge scientific research to help organisations set and implement robust evidence-based targets for mitigating and insetting impacts on climate, biodiversity, and society through nature-based solutions.


We are a passionate team of individuals spanning a broad range of research interests, experiences and backgrounds. Typically, we work with large corporates and financial institutions covering global supply chains and assets with large landscape-scale geographies. We are driven by impact, and work with organisations who are serious about ensuring their climate, biodiversity, social commitments and strategies are ambitious, credible, and net-zero aligned. 


The role 

At the heart of our work is our Nature Analytics framework, supported by a quantitative model. Our model allows us to baseline biodiversity impact across a given landscape within a supply chain, identify risks and dependencies, scenario model for specific interventions and provides for long term monitoring.


With demand for our work increasing, we are looking to expand the quantitative skills within our team, so that we can apply our analysis to a broader set of landscapes across the world. This is an exciting role for someone with a background in quantitative ecology and biodiversity, helping develop the methodology of our model and apply it directly to businesses to effect real change. 


You will be joining Nature-based Insights at a pivotal time in our development, with a real opportunity to have an impact on our direction and work - working alongside a passionate network of colleagues, partners, clients, who share your mission and values.


You will be working with a range of local and global datasets for complex natural resource supply chains, to help inform biodiversity strategies - using the best science with some of the biggest global supply chain companies. 


Who we are looking for


We are looking for an independent researcher interested in building innovative solutions for our client services. The right candidate will be a natural problem solver, passionate about incorporating the latest technologies and methods into their work. The ability to translate scientific insights into decision ready information for our clients is also crucial.


We are proud to support our employees in their career aspirations - we’re always open to discussing how the role can be adapted or evolve to best suit both parties. 


Responsibilities


  • Write, develop and calibrate R scripts to analyse specific geographies and landscapes.
  • Apply R to spatial mapping, and produce maps to help convey insights for clients.
  • Create written outputs, translating scientific analysis from your work into valuable insights for our clients.
  • Research and develop outputs from analysis compatible with emerging nature-reporting frameworks, e.g. TNFD, SBTN, CRSD.
  • Critically appraise our existing model, identifying gaps and providing ideas and insights for future development.
  • Work with the team to review and synthesise relevant literature to calibrate our model for specific contexts and scenarios.
  • Support the production and maintenance of our scientific methodology documentation.
  • Where appropriate, attend client meetings and presentations to provide scientific insight and advice.
  • Keep up to date with the latest in biodiversity datasets and monitoring technologies.
  • Depending on experience, contribute to monitoring and evaluation of NbS interventions in landscapes, including methodology design, implementation and analysis.




Skills, experience, qualifications


Essential:


  • Strong spatial and data analysis skills in R, includingterra,sfand data wrangling withtidyverse.
  • Experience in developing methodologies and conducting statistical analysis within biodiversity-related projects.
  • Excellent communication skills, both verbal and written across a range of mediums, including reports, articles and presentations, ideally for both academic and business audiences.
  • Experience of working within a team of researchers to project deadlines.
  • PhD in environmental, ecological or similar discipline, or have equivalent experience.
  • Willingness to travel internationally to supply chain landscapes.


Desirable:


  • Experience in using Google Earth Engine.
  • Experience with Shiny.
  • Ability to apply machine learning to LULC analysis.
  • Field ecology and monitoring experience. 
  • Understanding of global nature reporting frameworks such as TNFD and SBTN.
  • Experience within consultancy, ideally with corporate or financial institution clients.


What we offer:


  • Salary: £32,000
  • Remote first (UK-based), with allowance for co-working or homeworking set up, including budget to be used for desk, chair or any ergonomic equipment.
  • 25 days annual leave, plus public holidays.
  • Pension with ethical portfolio and salary sacrifice option.
  • All computer equipment, including laptop, screen and peripherals.
  • Networking and collaboration opportunities at University of Oxford.


How to apply

Please send your CV and covering letter to .


After shortlisting candidates, interviews will be conducted through a process of initial screening, formal interview, with potential for final subsequent interview, depending on the candidate pool. We aim to provide feedback for anyone invited to interview but unfortunately cannot extend this to non-shortlisted candidates.


We are looking to fulfil this role ASAP and applicants will be reviewed on a rolling basis. 


Nature-based Insights is committed to equality and value diversity. We particularly encourage application of women and those that come from minority backgrounds.

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Top 10 Best UK Universities for Machine Learning Degrees (2025 Guide)

Explore ten UK universities that deliver world-class machine-learning degrees in 2025. Compare entry requirements, course content, research strength and industry links to find the programme that fits your goals. Machine learning (ML) has shifted from academic curiosity to the engine powering everything from personalised medicine to autonomous vehicles. UK universities have long been pioneers in the field, and their programmes now blend rigorous theory with hands-on practice on industrial-scale datasets. Below, we highlight ten institutions whose undergraduate or postgraduate pathways focus squarely on machine learning. League tables move each year, but these universities consistently excel in teaching, research and collaboration with industry.

How to Write a Winning Cover Letter for Machine Learning Jobs: Proven 4-Paragraph Structure

Learn how to craft the perfect cover letter for machine learning jobs with this proven 4-paragraph structure. Ideal for entry-level candidates, career switchers, and professionals looking to advance in the machine learning sector. When applying for a machine learning job, your cover letter is a vital part of your application. Machine learning is an exciting and rapidly evolving field, and your cover letter offers the chance to demonstrate your technical expertise, passion for AI, and your ability to apply machine learning techniques to solve real-world problems. Writing a cover letter for machine learning roles may feel intimidating, but by following a clear structure, you can showcase your strengths effectively. Whether you're just entering the field, transitioning from another role, or looking to advance your career in machine learning, this article will guide you through a proven four-paragraph structure. We’ll provide practical tips and sample lines to help you create a compelling cover letter that catches the attention of hiring managers in the machine learning job market.

Veterans in Machine Learning: A Military‑to‑Civilian Pathway into AI Careers

Introduction Artificial intelligence is no longer relegated to sci‑fi films—it underpins battlefield decision‑support, fraud detection, and even supermarket logistics. The UK Government’s 2025 AI Sector Deal forecasts an additional £200 billion in GDP by 2030, with machine‑learning (ML) engineers cited as the nation’s second most in‑demand tech role (Tech Nation 2024). The Ministry of Defence’s Defence AI Strategy echoes that urgency, earmarking £1.6 billion for FY 2025–28 to embed ML into planning, logistics, and autonomous systems. If you have ever tuned a radar filter, plotted artillery trajectories, or sifted sensor data for actionable intel, you have already worked with statistical modelling—the backbone of machine learning. This guide shows UK veterans how to reframe military experience for ML roles, leverage MoD transition funding, and land high‑impact positions building the models shaping tomorrow’s defence and commercial landscapes. Quick Win: Bookmark our live board for Machine‑Learning Engineer roles to see who’s hiring today.