At a Glance
- Tasks: Develop and enhance statistical models for tropical cyclone risk assessment.
- Company: Join Moody's, a global leader in risk assessment and innovation.
- Benefits: Inclusive culture, competitive salary, and opportunities for professional growth.
- Other info: Collaborative team environment focused on cutting-edge research and methodologies.
- Why this job: Make a real impact on climate risk modelling and data-driven analytics.
- Qualifications: PhD or MSc in relevant fields with strong programming and analytical skills.
The predicted salary is between 60000 - 80000 £ per year.
At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence.
Skills and Competencies
- Strong expertise in statistical modelling, probability, and numerical methods, required to develop robust catastrophe risk models.
- Proficiency in programming languages such as Python, R, or Julia to build and maintain scalable modelling frameworks.
- Experience analysing climate and atmospheric data, with knowledge of tropical cyclone processes preferred.
- Ability to collaborate effectively in multidisciplinary teams, including use of version control systems (e.g., Git) for shared code development.
- Strong analytical thinking and problem-solving skills to interpret complex hazard behaviours and translate them into model outputs.
- Effective communication skills to present technical insights clearly to both technical and commercial stakeholders.
- Basic understanding of artificial intelligence concepts, with curiosity and enthusiasm for learning how AI tools can be used to improve processes and drive efficiency.
- Interest in exploring AI systems and a willingness to develop awareness of responsible AI practices, including risk management and ethical use.
Education
- PhD in Statistics, Atmospheric Science, Climate Science, Applied Mathematics, or a related discipline.
- MSc candidates with relevant industry experience may be considered.
Responsibilities
- Develop statistical and mathematical models to quantify tropical cyclone risk and support catastrophe risk assessment.
- Conduct research into tropical cyclone behaviour, including extreme event modelling, to quantify hazard and loss potential.
- Develop, test, and enhance statistical and numerical models used in catastrophe risk assessment.
- Collaborate with cross-functional teams across hazard, vulnerability, exposure, and financial modelling to ensure model accuracy and completeness.
- Analyse large-scale observational and reanalysis datasets to support model development and validation.
- Implement and maintain modelling code in high-performance computing environments where required.
- Translate complex modelling outputs into actionable insights for internal stakeholders and external clients.
- Contribute to continuous improvement of modelling methodologies, including exploration of advanced techniques such as machine learning.
About The Team
Our Model Development - Hazard Modelling team is responsible for building advanced catastrophe models that quantify the financial impact of natural hazards. We contribute to Moody’s by delivering industry-leading catastrophe modelling solutions used by global re/insurance and financial markets, and advancing scientific research and innovation in natural hazard risk modelling. By joining our team, you will be part of cutting-edge work in climate risk, catastrophe modelling, and data-driven analytics within a global, multidisciplinary environment.
Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law.
Catastrophe Modeller - Tropical Cyclones in London employer: PassFort
At Moody's, we pride ourselves on fostering an inclusive and innovative work culture that empowers our employees to thrive. As a Catastrophe Modeller focusing on Tropical Cyclones, you will have the opportunity to engage in cutting-edge research and collaborate with multidisciplinary teams, all while benefiting from continuous professional development and a commitment to ethical practices in AI. Located in a dynamic environment, Moody's offers a unique chance to contribute to meaningful projects that shape the future of risk assessment and management.
StudySmarter Expert Advice🤫
We think this is how you could land Catastrophe Modeller - Tropical Cyclones in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend events, and connect on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Prepare for interviews by practising common questions and showcasing your skills. We recommend doing mock interviews with friends or using online platforms. The more comfortable you are, the better you’ll perform!
✨Tip Number 3
Showcase your projects! If you’ve worked on any relevant modelling or data analysis projects, make sure to highlight them. We love seeing real-world applications of your skills, so don’t hold back!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always looking for passionate individuals who want to make a difference in risk assessment and modelling.
We think you need these skills to ace Catastrophe Modeller - Tropical Cyclones in London
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your expertise in statistical modelling and programming languages like Python or R. We want to see how your skills align with the role of a Catastrophe Modeller, so don’t hold back!
Tailor Your Application:Take a moment to customise your application for this specific role. Mention your experience with tropical cyclones and climate data analysis, as it’ll show us you’re genuinely interested in what we do at Moody's.
Be Clear and Concise:When writing your application, keep it straightforward. Use clear language to explain your past experiences and how they relate to the responsibilities of the position. We appreciate effective communication!
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at PassFort
✨Know Your Models
Make sure you brush up on your statistical modelling and numerical methods. Be ready to discuss how you've developed catastrophe risk models in the past, especially related to tropical cyclones. Having specific examples will show your expertise and confidence.
✨Show Off Your Coding Skills
Since programming is key for this role, be prepared to talk about your experience with Python, R, or Julia. You might even want to bring a small project or code snippet to demonstrate your skills. This will help you stand out as a candidate who can hit the ground running.
✨Communicate Clearly
You’ll need to present complex technical insights to both technical and non-technical stakeholders. Practice explaining your work in simple terms, focusing on how your models can translate into actionable insights. This will showcase your ability to bridge the gap between data and decision-making.
✨Stay Curious About AI
Moody's values innovation, so express your enthusiasm for AI and how it can enhance modelling processes. Share any experiences you have with machine learning or AI tools, and be ready to discuss how you see these technologies shaping the future of catastrophe modelling.
