Location
London

Hours
Full Time

About the Role
At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We strive 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.

As an Analytics & Modeling Analyst on the Inland Flood team, you will support the development and validation of inland flood catastrophe models used to quantify and manage flood risk globally. You will contribute to large-scale flood hazard model components including stochastic rainfall generation, rainfall-runoff processes, hydraulic routing, floodplain inundation, and flood defense failure mechanisms. Your work will involve calibrating and validating model components using observed datasets, benchmark studies, and published research to improve model performance and reliability.

You will analyze flood footprints, hazard metrics, and loss patterns using statistical methods, geospatial tools, and data visualization techniques. Additionally, you will research, evaluate, and implement innovative scientific and computational approaches to advance flood hazard modeling capabilities. This includes exploring, prototyping, and assessing artificial intelligence and machine learning methods for surrogate modeling, spatial downscaling, anomaly detection, feature extraction, emulator development, and workflow automation.

You will develop and maintain reproducible scientific workflows, data processing pipelines, and code repositories using modern software development practices. Collaboration with scientists, engineers, and data specialists to support model development, quality assurance, and scientific innovation is a key part of this role. You will communicate research findings and modeling insights to internal stakeholders, clients, and the broader scientific community through presentations, reports, and technical discussions.

Our Model Development team builds industry-leading natural catastrophe models that help quantify the financial impacts of natural hazards. The team brings together scientists, engineers, and data specialists working across flood, storm, and climate-related perils to advance risk analytics through cutting-edge research, large-scale modeling, and data innovation. By joining the Inland Flood team, you will contribute to high-impact scientific work in a collaborative environment that embraces the responsible use of artificial intelligence, machine learning, and advanced computational techniques to improve model performance and decision-making.

Experience
- Strong foundation in hydrology, hydraulics, climate science, applied mathematics, data science, computer science, or a related quantitative discipline
- Understanding of machine learning fundamentals including model training and validation, overfitting, and model evaluation
- Experience with one or more machine learning or deep learning approaches such as Long Short-Term Memory networks, Convolutional Neural Networks, and frameworks including PyTorch
- Practical programming experience in Python and familiarity with scientific computing environments; experience with R, C/C++, Fortran, CUDA, or Bash is preferred
- Ability to analyze, validate, and interpret large geospatial, hydrological, and climate datasets using statistical methods and visualization techniques
- Strong communication and collaboration skills with the ability to explain technical concepts to multidisciplinary stakeholders
- Experience working with high-performance computing environments, geographic information systems, structured query language, or data assimilation techniques is desired
- Basic understanding of artificial intelligence concepts, with curiosity and enthusiasm for learning how AI tools can be used to improve processes and drive efficiency
- Demonstrates a growing awareness of AI risk management and a commitment to responsible and ethical AI use

About you
Curious, collaborative, and motivated to contribute to scientific innovation and risk modeling. You value integrity and diversity of perspectives and are eager to apply advanced computational techniques to real-world challenges in flood risk assessment.

Qualifications
PhD in Hydrology, Hydraulics, Coastal and Ocean Engineering, Climate Science, Applied Mathematics, Data Science, Computer Science or a related quantitative discipline. Candidates with a relevant MSc and strong research or industry experience are also encouraged to apply.