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- Tasks: Lead AI and machine learning projects for weather and climate modelling.
- Company: Join the Met Office, a leader in climate science and innovation.
- Benefits: Enjoy a competitive salary, generous leave, and a fantastic pension scheme.
- Why this job: Make a real difference to our planet while advancing your career in science.
- Qualifications: Experience in machine learning and strong leadership skills required.
- Other info: Flexible working options and a supportive team environment await you.
The predicted salary is between 50000 - 60000 £ per year.
Join to apply for the Senior Scientist - Data Science for Simulation role at Met Office. We’ll look for a senior scientist who can help us make a difference to our planet. The role offers a base pay range up to £60,268 annually, a Civil Service pension, and annual leave of 27.5–32.5 days plus bank holidays.
About the Role
You will contribute to research and development in artificial intelligence and machine learning for weather and climate modelling, with an initial focus on advancing the FastNet Machine Learning Weather Prediction model as part of the AI4NWP project. You will operate within a multidisciplinary team of data scientists, engineers and domain experts, collaborating with National Meteorological Services, research institutions and commercial organisations.
Responsibilities
- Lead the scientific development and implementation of machine learning solutions for Earth system applications, focusing on weather forecasting.
- Apply software engineering best practices to ensure scientific code is reproducible, scalable, and maintainable; design robust data workflows and optimise advanced machine learning methods for weather and climate data.
- Communicate research findings through peer‑reviewed publications and presentations at conferences and seminars.
- Collaborate with internal and external domain experts and share expertise within the broader scientific and research software engineering community.
- Mentor junior scientists and help shape the strategic direction of the team.
Essential Criteria, skills and experience
- Experience in developing machine learning solutions in the atmospheric sciences or related domains.
- Ability to provide scientific leadership, generate innovative solutions to complex challenges, contribute to strategic planning and lead the delivery of complex research projects, including leading the work of others.
- Proficiency in software engineering and knowledge of quality assurance practices.
- Excellent interpersonal and communication skills, with proven ability to listen actively, discuss complex scientific and/or technical information clearly and effectively with a range of audiences, and experience of working in collaborative, cross‑disciplinary research environments.
- Strong organisational skills and ability to work both independently and collaboratively to deliver work on time and aligned with agreed project and organisational objectives.
- Commitment to ongoing personal professional development and to supporting others in developing theirs, leading to improved outputs and the career development of others.
Benefits
- Outstanding Civil Service pension (average employer contribution 28.97%).
- Annual leave 27.5 days (plus bank holidays) increasing to 32.5 days (plus bank holidays) after five years.
- Option to buy or sell up to 5 days per year of annual leave.
- Hybrid working arrangement available.
- Standard full‑time 37‑hour week or flexible 30‑hour work.
How to apply
If you share our values, we’d love to hear from you! Click apply to begin your application. Complete your career history and provide evidence against each essential criterion in the supporting statement questionnaire. We recommend candidates use the CARL method (Context, Action, Result and Learning) for presenting evidence of experience and skills. Closing date: 27/01/2026 at 23:59. Interviews commence from 02/02/2026. You will hear from us once the closing date has passed.
Using AI in your application
We welcome applications that use AI tools for support in drafting or refining, as long as they accurately reflect your own skills and experience. All hiring decisions at the Met Office are made by people, not AI.
How we can help
If you have any questions or would like to discuss this opportunity further, please contact us. If you’re considering applying and need support to do so, you can request adjustments either within your application or by contacting us. Should you be offered an interview, there may be a selection exercise which could include a presentation, written test or a scenario‑based activity. You can select in your application to be considered under the Disability Confident Scheme. We understand that great minds don’t always think alike and, as an equal opportunities employer, we welcome applications from those with all protected characteristics. We recruit on merit, fairness and open competition in line with the Civil Service Code.
We can only accept applications from those eligible to live and work in the UK – please refer to GOV.UK for information. We require security clearance, for which you need to have resided in the UK for at least 3 of the last 5 years, with 2 of these years immediately preceding the point of your application. You will need to achieve full security clearance within your first 6 months with us.
Senior Scientist - Data Science for Simulation employer: Met Office
Contact Detail:
Met Office Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Scientist - Data Science for Simulation
✨Tip Number 1
Network like a pro! Reach out to professionals in the field of data science and weather modelling. Attend conferences, webinars, or local meetups to connect with potential colleagues and mentors who can give you insights and possibly refer you for roles.
✨Tip Number 2
Showcase your skills! Create a portfolio that highlights your machine learning projects, especially those related to atmospheric sciences. This will not only demonstrate your expertise but also give you something tangible to discuss during interviews.
✨Tip Number 3
Prepare for interviews by practising common questions related to data science and machine learning. Use the CARL method (Context, Action, Result, Learning) to structure your answers, making it easier for interviewers to see your thought process and problem-solving skills.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets the attention it deserves. Plus, you’ll find all the details you need about the role and how to tailor your application to stand out.
We think you need these skills to ace Senior Scientist - Data Science for Simulation
Some tips for your application 🫡
Show Your Passion: When you're writing your application, let your enthusiasm for data science and climate modelling shine through. We want to see how your passion aligns with our mission to make a difference to the planet!
Use the CARL Method: Remember to structure your supporting statement using the CARL method (Context, Action, Result, Learning). This helps us understand your experiences clearly and shows how you've tackled challenges in your previous roles.
Tailor Your Application: Make sure to tailor your application to highlight your experience in machine learning and atmospheric sciences. We’re looking for specific examples that demonstrate your skills and how they relate to the role.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way to ensure your application gets to us directly. Plus, you can find all the details you need about the role and our values there.
How to prepare for a job interview at Met Office
✨Know Your Stuff
Make sure you brush up on your machine learning and atmospheric sciences knowledge. Be ready to discuss specific projects you've worked on, especially those that relate to weather forecasting. This will show your expertise and passion for the field.
✨Showcase Your Leadership Skills
Since this role involves leading complex research projects, be prepared to share examples of how you've successfully led teams or initiatives in the past. Use the CARL method to structure your responses, highlighting the context, actions you took, results achieved, and what you learned.
✨Communicate Clearly
Practice explaining complex scientific concepts in simple terms. You’ll need to communicate effectively with a range of audiences, so think about how you can make your research accessible. Consider doing mock interviews with friends or colleagues to refine your communication style.
✨Prepare for Collaboration
This role requires working with multidisciplinary teams, so be ready to discuss your experience in collaborative environments. Think of examples where you’ve successfully worked with others, shared expertise, or mentored junior scientists. Highlighting your interpersonal skills will be key!
