Catastrophe Modeller - Tropical Cyclones

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. If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity. 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