Analytics & Modeling Analyst, Flood Modeller

About the Team

Our Model Development team brings together scientists, engineers, and data specialists to build natural catastrophe models. The Inland Flood team develops high-resolution, large-scale hydrologic and hydraulic models. As AI and machine learning evolve, we integrate them selectively alongside established physical and statistical approaches to improve model development, data processing, quality assurance, and scientific insight.

About the Role

Join a collaborative scientific team building high-resolution, large-scale hydrologic and hydraulic models and contribute to model components, data pipelines, validation, and research.

Key Responsibilities

• Contribute to the development and application of large-scale flood hazard model components, including elevation models and other geospatial input layers, stochastic rainfall, rainfall-runoff processes, hydraulic routing, floodplain inundation, and flood-defence failure mechanisms.
• Calibrate and validate model components with a focus on GIS, using observed data, published research, and benchmark datasets.
• Evaluate model outputs such as flood footprints, hazard metrics, and loss patterns using statistical methods, visualization, and GIS tools.
• Advance flood model input layer preparation, including elevation models, land use data, and remotely sensed data, through research, evaluation, and implementation of novel scientific and computational techniques.
• Explore, prototype, and evaluate AI and ML methods for model-development challenges including surrogate modelling, spatial downscaling, feature extraction, anomaly detection, data processing, emulator development, and workflow automation.
• Contribute to the development of reproducible scientific workflows, code repositories, and data-processing pipelines using modern software development practices.
• Communicate research findings and modelling insights to internal stakeholders, clients, and the broader scientific community.
• Contribute to an inclusive, collaborative culture through knowledge sharing, constructive review, and continuous learning.
• Demonstrate proficiency in artificial intelligence concepts, with hands-on experience using AI tools to streamline workflows and enhance operational efficiency.
• Proven ability to implement AI-powered solutions to solve business challenges.
• Demonstrates a growing awareness of AI risk management and a commitment to responsible and ethical AI use.

Requirements & Tech Stack

• Practical programming experience in Python.
• Demonstrated interest in ML, flood modelling, environmental systems, geospatial analytics, or related scientific applications.
• Ability to communicate technical concepts clearly and collaborate effectively in multidisciplinary teams.

Qualifications

• MSc in GIS, Hydrology, Hydraulics, Coastal and Ocean Engineering, Climate Science, Applied Mathematics, Data Science, Computer Science, or a related quantitative discipline.

Advantageous Skills & Experience

• Familiarity with scientific computing environments.
• Experience with R, C/C++, Fortran, CUDA, or bash.
• Understanding of machine learning fundamentals, including model training and validation, overfitting, and model evaluation.
• Experience with one or more deep learning models such as LSTMs or CNNs and frameworks such as PyTorch.
• Knowledge of elevation model refining strategies, such as cleaning of LiDAR data and artefact removal.
• Familiarity with large-scale model development projects on HPC environments in Linux.
• Experience working with geospatial foundation models.