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- Tasks: Analyse climate data and build models to manage climate risk in financial services.
- Company: Join a mission-driven team focused on tackling real-world climate challenges.
- Benefits: Enjoy a flexible hybrid work model, competitive salary, and career growth opportunities.
- Why this job: Make a real impact by turning complex data into actionable insights for the insurance sector.
- Qualifications: Experience in data science with proficiency in Python or R; degree in a quantitative field required.
- Other info: Work with industry-leading datasets and collaborate with top clients in climate risk assessments.
The predicted salary is between 36000 - 60000 £ per year.
Location: London, UK
Employment Type: Full-time | Hybrid
We're looking for a talented Data Scientist / Senior Data Scientist with a passion for climate risk and geospatial data to join our clients growing analytics team in London. If you're excited about turning complex environmental datasets into actionable insights for the FS sector, we want to hear from you.
About the Role
In this role, you'll work at the intersection of climate science, geospatial analysis, and insurance risk modeling, helping their clients better understand and manage the impact of physical climate risk. You'll 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 FS 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 Scientist (Financial Services) employer: PureFuel
Contact Detail:
PureFuel Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist (Financial Services)
✨Tip Number 1
Familiarise yourself with the latest climate science and ESG regulations. This knowledge will not only help you understand the role better but also demonstrate your commitment to staying updated in a rapidly evolving field.
✨Tip Number 2
Network with professionals in the climate risk and geospatial analysis sectors. Attend relevant conferences or webinars, and engage with industry experts on platforms like LinkedIn to gain insights and potentially get referrals.
✨Tip Number 3
Showcase your practical experience with large-scale geospatial data and machine learning techniques. Consider working on personal projects or contributing to open-source initiatives that highlight your skills in these areas.
✨Tip Number 4
Prepare to discuss specific examples of how you've applied data science in real-world scenarios, particularly related to climate or catastrophe risk. This will help you stand out during interviews and show your problem-solving capabilities.
We think you need these skills to ace Data Scientist (Financial Services)
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 or roles that demonstrate your skills in these areas.
Craft a Compelling Cover Letter: In your cover letter, express your passion for climate risk and how your background aligns with the role. Mention specific tools and techniques you’ve used, such as Python or R, and how they relate to the job description.
Showcase Relevant Projects: If you have worked on projects involving climate datasets or machine learning applications, be sure to include them in your application. Describe your role, the challenges faced, and the outcomes achieved.
Highlight Continuous Learning: Mention any recent courses, certifications, or workshops related to climate science, geospatial analysis, or data science. This shows your commitment to staying updated with industry trends and regulations.
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 technical problem or explain your approach to data analysis.
✨Familiarise Yourself with Relevant Models
Brush up on climate models like CMIP6 or ERA5, as well as catastrophe models. Being able to discuss these models and their applications in risk assessment will show your depth of knowledge in the field.
✨Prepare for Scenario-Based Questions
Expect questions that assess your ability to apply machine learning techniques to real-world scenarios. Think about how you would analyse specific datasets or visualise climate risk data for different audiences.
