Analytics & Modeling Analyst (Inland Flood)

In this role you will contribute to inland flood risk modeling within Moody’s Model Development team, applying advanced hydrology, climate science, and ML methods to quantify global flood risk. You’ll collaborate with scientists and engineers to develop scalable, reproducible modeling workflows and explore AI techniques for surrogate modeling and automation. This is a chance to shape cutting-edge flood hazard insights and support data-driven decision making for clients. You’ll work in a cross-disciplinary, innovation-driven environment that values responsible AI use.

• Support the development and validation of inland flood catastrophe models
• Develop large-scale flood hazard model components (stochastic rainfall, rainfall-runoff, hydraulic routing, floodplain inundation, defense failure)
• Calibrate and validate model components using observed data and literature
• Analyze flood footprints, hazard metrics, and loss patterns using statistical and geospatial tools
• Research and implement innovative computational approaches to advance flood hazard modeling
• Prototype and assess AI/ML methods for surrogate modeling and workflow automation
• Maintain reproducible scientific workflows and code repositories
• Collaborate with scientists, engineers, and data specialists to ensure model quality and scientific innovation
• Communicate findings to stakeholders and clients through presentations and reports

• 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/validation, overfitting, and model evaluation
• Experience with ML or deep learning approaches such as LSTM, CNNs; familiarity with PyTorch
• Programming experience in Python; familiarity with R, C/C++, Fortran, CUDA, or Bash is preferred
• Ability to analyze and interpret large geospatial, hydrological, and climate datasets using statistics and visualization
• Strong communication and collaboration skills to explain technical concepts to multidisciplinary stakeholders
• Experience with high-performance computing environments, GIS, SQL, or data assimilation is desired
• Basic understanding of AI concepts and interest in applying AI to improve processes and efficiency
• Growing awareness of AI risk management and ethical AI use

• Strong communication
• Collaboration
• Curiosity
• PyTorch
• Python
• R