Hiring.Camp

Environmental Data Scientist

Ceh

·

Today

Salary
£41k – £43k/yr
Location
Lancaster, United Kingdom
Workplace
Hybrid
Type
Full-time
Department
IT
Education
PhD
Visa
Not sponsored
Closing date
Today
Source
Workday

Description

Salary - £40,574 - £43,056
Hybrid working (50/50)
Permanent, full-time (37 hours per week)
Closing date for applications: Thursday 26 November 2026. We reserve the right to close this advert early if we find the right candidate, so we encourage you to apply early.

An exciting opportunity for an Environmental Data Scientist to join our Digital Innovation group at UKCEH Lancaster to work on delivering the cutting-edge data science, Artificial Intelligence (AI) and analytical components of the UKCEH Digital Strategy across our monitoring and modelling portfolio and integrating these into our Digital Research Infrastructure (DRI).


The DRI at UKCEH supports the full lifecycle of environmental science projects, from monitoring and processing data to analysing and visualising data for decision-making under uncertainty. This involves a range of services for stakeholders, and most importantly for this role, the Environmental Data Science Toolbox – enhancing the discoverability and reuse of data science and AI methods across the environmental community. Other tools include data repositories such as the Environmental Information Data Centre (EIDC) and virtual laboratories for scientific collaborations (such as DataLabs).


You will play a crucial role in developing innovative approaches to exploit and integrate the data we collect, enhancing its discoverability within the wider environmental science community and explore how the latest advancements in Artificial Intelligence (AI) can be used to support systems thinking approaches to environmental science challenges (E.g. One Health, Data Centre Sustainability). You will be involved in working with a variety of digital objects (e.g., models, methods, workflows) from across the UKCEH portfolio, leveraging the latest data science and AI techniques (ranging from machine learning to large language models). This will enable us to address increasingly complex scientific questions and improve the efficiency and sustainability of our processes.


Your main responsibilities will include:

  • Use state-of-the-art data science and AI techniques to investigate new ways of integrating a broad range of data and other digital objects available in the UKCEH portfolio and beyond (E.g. Citizen science data, sensor networks and large process-based models.)
  • Research and develop novel and reusable approaches and associated methods to enhance discoverability of UKCEH’s digital objects, specifically large scale long-term environmental data
  • Build on techniques such as natural language processing, spatial analysis, and machine learning to develop data science methods and analytical workflows to drive forwards whole systems approaches to tackling environmental science challenges (E.g. One Health, Data Centre Sustainability)
  • Analyse, reflect and respond to the environmental impact of the data science approaches you use and develop.
  • Work in the Digital Innovation group, and closely with the Digital Engineering, and Data and Digital Integration groups to make data science methods and analytical workflows available for the community within DRI (e.g., through innovations such as the Environmental Data Science Toolbox).
  • Connect and collaborate with researchers to harness the power of collaborative digital platforms and foster transdisciplinary research across the organisation and the wider environmental data science community.
  • Support opportunities to enhance data science methods with the research community, including publishing your research and contributing to relevant research proposals.

Additionally, you will ensure these techniques are incorporated into UKCEH’s DRI to support the integration of future data and digital objects. This will involve close collaboration with the ‘Digital Engineering’ and ‘Data and Digital Integration’ groups within UKCEH's National Capability and Digital Research science area, as well as linking up with UKCEH’s wide range of science leadership across land, water and air.


We're looking for someone who enjoys getting to grips with complex environmental data and finding better ways to bring different datasets together. You’ll ask thoughtful questions and work closely with scientists across UKCEH and its partner organisations to understand the research challenges they face. You’ll bring curiosity and critical thinking to testing new approaches, and see promising ideas through to practical digital tools and methods that researchers can use and build on.



What we’re looking for someone who has:


  • A degree in a relevant subject (e.g., statistics, data science, environmental science or computer science), as well as a PhD, or equivalent relevant experience
  • Has evidence of continuing professional development and up to date knowledge of research in their field, including research experience of applying data science and AI skills, ideally to an environmental context or for whole systems approaches to research challenges
  • Demonstrable scientific programming skills for example R, Python, Julia or equivalent.
  • Proven experience with using machine learning approaches and natural language processing techniques, desirably with an understanding of their environmental impacts
  • An aptitude for learning new technologies and techniques for integrating, sharing and enhancing discoverability of data and other digital objects
  • Ability to effectively present to and collaborate with people from different areas of expertise/scientific domains and work well within a team
  • An ability to process and analyse data in a robust and reproducible manner using the latest collaborative platforms (e.g. Notebooks technologies or virtual lab environments)



What will help you stand out:


  • A curious and collaborative approach with scientists and other stakeholders, asking thoughtful questions to understand their research challenges and shape solutions around their needs
  • The ability to bring together and analyse varied, complex environmental datasets to address research questions.
  • An innovative approach; evaluating existing approaches, testing new methods and turning promising ideas into practical digital tools that researchers can use and build on.



How to apply and what happens next

If you’re interested in joining us, we’d love to hear from you. Apply through our Workday portal by uploading your CV and answering the three questions below. A separate cover letter is not required.


Please keep each response to 150 words or fewer, using clear examples that highlight your own contribution and relevant experience.



  • Q1. How could advances in AI and data science change the way researchers share and make use of environmental data? Drawing on a method you’ve used or evaluated, tell us where you see the greatest opportunity and what you’d want to test before putting it into practice.

  • Q2. Tell us about an analytical method or workflow you made easier for others to reuse. What was the hardest barrier to overcome, and how did you judge whether someone else could pick up and adapt your work?

  • Q3. Tell us about a time you helped people from another discipline make sense of a complex technical method. How did you find out what mattered to them, adapt your explanation and help them decide whether the method could answer their question?

What happens next?

Our panel will review your application, and we are committed to providing an outcome to all applicants.




Working at UKCEH is genuinely rewarding

You’ll be joining a leading independent, not-for-profit research institute, where your work will have real impact. Our science helps people and the environment to prosper and informs decisions that benefit society as a whole. We’re proud to be custodians of over 60 years of environmental data, and just as proud of the people behind it. You’ll be supported to develop your career, grow your skills and thrive in a collaborative, purpose-driven environment.


What we offer

At UKCEH, we recognise your dedication and provide a comprehensive benefits package designed to support you both at work and at home. When you join us, you’ll benefit from:


  • Generous annual leave - 27 days (rising to 29 days after 5 years) +3 for Christmas closure
  • 10% employer pension contribution
  • Financial protection through life assurance and income protection
  • Enhanced family-friendly policies
  • Flexible and hybrid working options (depending on your role)
  • Lifestyle perks including dental, gym, retail and cycling discounts
  • A commitment to wellbeing, supported by a range of internal initiatives
  • Employee Assistance Programme and health benefits, including 24/7 support with remote GP access, mental health, physiotherapy and lifestyle services
  • Peer recognition and rewards that celebrate your contribution
  • Salary sacrifice agreement benefits including Electric & Plug-in Hybrid vehicle and Cycle2Work schemes
  • Interest-free annual travel season ticket loans (terms apply)



Our commitment to inclusion

At UKCEH, we believe everyone should feel welcome and able to be themselves at work. We warmly encourage applications from people of all backgrounds, experiences and identities. As a Disability Confident employer, we’re happy to support you with any adjustments you may need throughout the recruitment process.


Our approach to AI and applications

We understand that candidates may use AI tools to support their application. We encourage you to ensure your submission reflects your own experience, skills and suitability for the role. Every application that meets the essential criteria will be reviewed by our recruitment panel.


Visa sponsorship is not available for this role, and the role is not eligible for endorsement under the Global Talent visa route. If you already have, or intend to obtain, the right to work in the UK through another visa route that does not require UKCEH sponsorship, please tell us in your application.

Skills

PythonJuliaMachine LearningData ScienceWorkdayLLM

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