Tropical Cyclone Risk Modeler — AI-Driven Analytics

Moody's; Greater London; On-site; GBP 60,000 - 80,000; Full time

Job summary

Moody's Investors Service in Greater London is seeking a candidate to develop statistical models for tropical cyclone risk assessment. The ideal candidate will have a PhD in a relevant field and strong skills in statistical modelling and programming (Python, R, or Julia). Responsibilities include conducting research on tropical cyclone behavior, collaborating with cross-functional teams, and implementing advanced modelling methodologies. Join a dedicated team advancing catastrophe modelling and climate risk analytics in a multidisciplinary environment.

Qualifications

• Strong expertise in statistical modelling to develop catastrophe risk models.
• Proficiency in programming languages like Python, R, or Julia.
• Experience analysing climate and atmospheric data preferred.

Responsibilities

• Develop statistical models to quantify tropical cyclone risk.
• Conduct research into tropical cyclone behaviour for hazard and loss potential.
• Collaborate with cross-functional teams for model accuracy.

Skills

Statistical modelling expertise

Proficiency in Python, R, or Julia

Experience analysing climate data

Collaborative skills with version control systems

Analytical thinking

Effective communication skills

Understanding of AI concepts

Interest in responsible AI practices

Education

PhD in Statistics, Atmospheric Science, Climate Science, or related discipline

MSc with relevant industry experience

Job description

Moody's Investors Service in Greater London is seeking a candidate to develop statistical models for tropical cyclone risk assessment. The ideal candidate will have a PhD in a relevant field and strong skills in statistical modelling and programming (Python, R, or Julia). Responsibilities include conducting research on tropical cyclone behavior, collaborating with cross-functional teams, and implementing advanced modelling methodologies. Join a dedicated team advancing catastrophe modelling and climate risk analytics in a multidisciplinary environment.