Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Analyse climate data and build models to manage climate risk in financial services.
- Company: Join a mission-driven analytics team focused on tackling climate challenges in London.
- Benefits: Enjoy a flexible hybrid work model, competitive salary, and career growth opportunities.
- Why this job: Make a real impact on climate risk while working with cutting-edge datasets and tools.
- Qualifications: Experience in data science, proficiency in Python or R, and a degree in a quantitative field.
- Other info: Collaborate with top clients in the insurance sector on climate-related assessments.
The predicted salary is between 28800 - 48000 £ per year.
We are looking for a talented Data Scientist / Senior Data Scientist with a passion for climate risk and geospatial data to join our client's growing analytics team in London. If you are excited about turning complex environmental datasets into actionable insights for the financial services sector, we want to hear from you.
About the Role
In this role, you will work at the intersection of climate science, geospatial analysis, and insurance risk modeling, helping clients better understand and manage the impact of physical climate risk. You will be building scalable models and tools that directly support underwriting, portfolio risk management, and strategic planning in a changing climate.
What You’ll Do
- Analyse and model climate and natural catastrophe datasets (e.g. flood, wildfire, storm, sea-level rise)
- Work with large-scale geospatial data (satellite imagery, GIS layers, remote sensing)
- Apply machine learning techniques to identify risk patterns and trends
- Develop tools to visualise and interpret climate risk data for technical and non-technical audiences
- Collaborate with insurance and reinsurance clients on climate-related risk assessments
- Stay on top of the latest climate science and ESG regulations impacting the financial services industry
What We’re Looking For
- Experience in data science, ideally in climate, geospatial, or catastrophe risk
- Proficiency in Python, R, or similar, with experience using libraries e.g. pandas, scikit-learn
- Experience with climate models (e.g. CMIP6, ERA5) or catastrophe models is a strong plus
- Degree in a quantitative field: data science, climatology, environmental science, geoinformatics, or similar
Why Join Us?
- Be part of a mission-driven team tackling real-world climate challenges
- Work with industry-leading datasets and tools
- Flexible hybrid work model (central London office)
- Competitive salary, bonus, and benefits package
- Career growth opportunities in a rapidly expanding area of climate risk analytics
Data Analysis Scientist - Machine Learning employer: PureFuel
Contact Detail:
PureFuel Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Analysis Scientist - Machine Learning
✨Tip Number 1
Familiarise yourself with the latest climate science and ESG regulations. This knowledge will not only help you understand the context of the role better but also demonstrate your commitment to staying updated in a rapidly evolving field.
✨Tip Number 2
Engage with online communities or forums related to climate risk and geospatial analysis. Networking with professionals in the field can provide insights into industry trends and may even lead to referrals for job opportunities.
✨Tip Number 3
Showcase your practical experience with machine learning techniques by working on personal projects or contributing to open-source initiatives. This hands-on experience can set you apart from other candidates and highlight your skills effectively.
✨Tip Number 4
Prepare to discuss specific examples of how you've applied data science in real-world scenarios, particularly in relation to climate or geospatial data. Being able to articulate your past experiences will make a strong impression during interviews.
We think you need these skills to ace Data Analysis Scientist - Machine Learning
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data science, particularly in climate, geospatial, or catastrophe risk. Use specific examples of projects where you've applied machine learning techniques or worked with large datasets.
Craft a Compelling Cover Letter: In your cover letter, express your passion for climate risk and how your skills align with the role. Mention any specific experiences that demonstrate your ability to analyse climate data and develop actionable insights.
Showcase Technical Skills: Clearly list your proficiency in programming languages like Python or R, and mention any relevant libraries you have used, such as pandas or scikit-learn. Provide examples of how you've applied these skills in previous roles.
Highlight Collaborative Experience: Since the role involves collaboration with clients, emphasise any past experiences where you've worked in teams or with external stakeholders on climate-related projects. This will show your ability to communicate complex data to both technical and non-technical audiences.
How to prepare for a job interview at PureFuel
✨Show Your Passion for Climate Science
Make sure to express your enthusiasm for climate risk and geospatial data during the interview. Share any relevant projects or experiences that highlight your commitment to understanding and addressing climate challenges.
✨Demonstrate Technical Proficiency
Be prepared to discuss your experience with programming languages like Python or R, especially in relation to libraries such as pandas and scikit-learn. You might be asked to solve a problem or explain how you've used these tools in past projects.
✨Prepare for Case Studies
Expect to work through case studies or scenarios related to climate risk analysis. Practise explaining your thought process clearly, as you'll need to demonstrate how you approach complex datasets and derive actionable insights.
✨Stay Updated on Industry Trends
Familiarise yourself with the latest developments in climate science and ESG regulations. Being able to discuss current trends will show that you're proactive and knowledgeable about the field, which is crucial for this role.
