Spatial Analyst (Remote - UK only)

Nature-based Insights
Sheffield
2 months ago
Applications closed

Related Jobs

View all jobs

Biomarker Data Analyst

GIS Technician

Data Scientist

Apply in 3 Minutes! We are hiring: Data Scientist,London ...

▷ [Only 24h Left] We are hiring: Data Scientist,London ...

[Urgent Search] We are hiring: Data Scientist, LondonGeolytix ...

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.

Rural-Remote Machine Learning Jobs: Finding Balance Beyond the Big Cities

Over the past decade, machine learning (ML) has transformed from a niche research domain into a pervasive technology underpinning everything from recommendation systems and voice assistants to financial forecasting and autonomous vehicles. Historically, the UK’s major tech hubs—particularly London—have been magnets for top ML talent and corporate headquarters. However, remote work has become mainstream, and many ML professionals are realising they can excel in their field while living far beyond the city limits. At MachineLearningJobs.co.uk, we’ve observed a growing interest in positions that allow for a rural lifestyle or a coastal environment, often reflected in search terms like “ML remote countryside” or “tech jobs by the sea.” This surge is no coincidence. Flexible work policies, better rural broadband, and the nature of machine learning tasks—much of which can be done through cloud platforms—are bringing new opportunities to those who wish to swap urban hustle for fresh air and scenic views. Whether you’re a data scientist, ML engineer, researcher, or product manager, a rural or seaside move could reinvigorate your work-life balance. In this article, we’ll unpack why rural-remote ML jobs are on the rise, how you can navigate the challenges of leaving the city, and what you need to do to thrive in a machine learning career beyond the M25. If you’ve dreamt of looking up from your laptop to rolling fields or ocean waves, keep reading—your rural ML role might be closer than you think.

Quantum-Enhanced Machine Learning—Propelling AI into the Next Frontier

Machine learning (ML) has revolutionised how we interpret data, build predictive models, and create intelligent applications. From recommendation engines and self-driving cars to advanced genomics and natural language processing, ML solutions are integral to nearly every corner of modern life. However, as data complexity and model size continue to skyrocket, the computational demands placed on ML systems grow in tandem—often pushing even high-performance classical computers to their limits. In recent years, quantum computing has emerged as a tantalising solution to these challenges. Unlike traditional digital systems, quantum computers exploit quantum mechanics—superposition and entanglement—to process information in ways that defy conventional logic. As these machines mature, they promise exponential speed-ups for certain tasks, potentially reshaping how we approach AI and data-intensive challenges. What does this mean for machine learning? Enter quantum-enhanced ML, a new frontier where quantum processors and classical ML frameworks unite to accelerate model training, tackle high-dimensional data, and solve complex optimisation tasks more efficiently. In this article, we will: Unpack the current state of machine learning, highlighting key bottlenecks. Provide a concise overview of quantum computing—why it’s radical and how it differs from classical technology. Examine potential breakthroughs in quantum-enhanced ML, including real-world use cases and technical approaches. Explore the roles and skill sets that will define this quantum-AI era, with guidance on how to prepare. Discuss the roadblocks (like hardware maturity and ethical concerns) and how they might be addressed in the years to come. If you’re a machine learning engineer, data scientist, or simply an AI enthusiast fascinated by the next wave of computational innovation, read on—quantum computing could become an integral part of your future toolkit, opening up job opportunities and reimagining what ML can achieve.

Machine Learning Jobs at Newly Funded UK Start-ups: Q3 2025 Investment Tracker

Machine learning (ML) has become the beating heart of modern tech innovation, powering breakthroughs in healthcare, finance, cybersecurity, robotics, and more. Across the United Kingdom, this surge in ML-driven solutions is fueling the success of countless start-ups—and spurring demand for talented machine learning engineers, data scientists, and related professionals. If you’re eager to join a high-growth ML company or simply want to keep tabs on the latest trends, this Q3 2025 Investment Tracker will guide you through the newly funded UK start-ups pushing the boundaries of ML. In this article, we’ll highlight key developments from Q3 2025, delve into the most promising newly funded ventures, and shed light on the machine learning roles they’re urgently seeking to fill. Plus, we’ll show you how to connect with these employers via MachineLearningJobs.co.uk, a dedicated platform for ML job seekers. Let’s dive in!