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.
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.
Moody’s RMS is the world's leading provider of mathematical models and information related to the financial impact of natural catastrophes. Our Model Development department has a multidisciplinary team of scientists and engineers, building mathematical models that predict damage caused by tropical storms, extra-tropical storms, thunderstorms, coastal floods, freshwater floods and tsunamis.
We use a combination of observed data, reanalysis data, numerical, statistical and engineering models and data assimilation. We are the pioneers in the development and application of complex statistical and numerical modelling methods for the quantification of natural hazard risk, and our risk models are the most detailed and comprehensive models of natural catastrophes produced anywhere in the world.
Role & Responsibilities
The Inland Flood team focuses on developing high-resolution, large-scale hydrologic/hydraulic flood model. The work carried out by the flood team encompasses all steps from hazard to loss modeling. The department has an engaged, collaborative working environment with a clear scientific and innovative culture.
We are looking for a candidate with a background in hydrology, climate sciences, data sciences, or a closely related field and a desire to apply that expertise in quantifying the risks driven by inland flood.
Key accountabilities & deliverables:
Contribute to the scientific and technological development of the large scale flood hazard model components, as well as to their application: space-time stochastic rainfall model, rainfall-runoff processes, hydraulic routing modelling, high-performance models for floodplain inundation and defense failure mechanisms Further advancement of flood hazard models by researching and implementing novel scientific techniques (e.g. Machine learning in flood prediction, inundation, forecasting etc.) Calibrate and validate model components throughout the comparison to observed data including benchmarking of flood inundation maps Evaluate final model output (e.g., flood footprints, flood losses and their spatial patterns using statistical methods and GIS tools) Advancement of our models through research and implementation of novel scientific techniques (e.g. meteorological drivers of extreme floods, large scale patterns of flooding, trends and impact of regional flood risk, etc..) Communicating research to clients and other stakeholders across teams
Qualifications:
PhD in Hydrology, Hydraulics, Coastal and Ocean Engineering, Applied Mathematics, or a related discipline. Strong candidates with a relevant MSc and appropriate research or work experience will also be considered Strong programming ability in scientific and analytical scripting/programming languages (e.g. Fortran, R, Python, bash, CUDA, C/C++) Strong analytical skills and ability to effectively communicate insights to internal and external stakeholders
Advantageous Skills:
Knowledge of Machine Learning techniques applied to large environmental datasets Experience working with large scale climate model datasets (e.g. GCMs, RCMs, or CORDEX) Familiarity with large scale model development projects on HPC environments in Linux Understanding of data-assimilation and/or forecasting environments, GIS tools, SQL and a strong publication record Strong knowledge of statistics and probability theory
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.
