Statistical Modeler in Wildfire Team

Statistical Modeler in Wildfire Team We are looking for a Statistician to join our Impact Forecasting Wildfire team. This is a hands‑on statistical modeling and assurance role within a specialized wildfire team, passionate about ensuring our quantitative methods are sound, modern, and aligned with best practice. The ideal candidate combines mathematical and statistical training, proven programming skills, and a genuine curiosity about applying sophisticated methods to environmental and catastrophe modeling. What the day will look like Contribute to the design, analysis, and validation of models, ensuring methods and outputs are robust and transparent. Develop and maintain statistical and machine learning models using Python, R, or a combination of both, applying modern development practices and tools (Git, AI‑assisted coding). Handle large and complex datasets, including cleaning, feature engineering, exploration, and diagnostics. Apply and adapt advanced methods (spatial, time series or extreme value models), integrate them into modeling workflows in collaboration with scientists, GIS specialists, and engineers. Validate and verify studies against historical observations, and document for internal and client‑facing audiences. How this opportunity is different This is an engaging role in a dynamic, friendly team where you can build focused expertise through continuous international training and potential travel. At Aon, a global leader in reinsurance with regional headquarters in central Prague, you’ll work with colleagues around the world in an environment that values integrity, authenticity, and a can‑do approach. We offer flexible working, home office options, and an attractive benefits package including pension contributions, in‑office massages, language courses, ergonomic desks, sick days, and more.

That Will Lead to Success University degree in statistics, applied mathematics, or a related field, with a solid base in probability, inference, and statistical modeling and relevant working experience Proficient programming skills in Python and/or R, writing efficient, readable code and using modern tooling (e.g., Git, GitHub, AI‑assisted coding tools). Experience with modern machine learning methods and their appropriate use and limitations. Demonstrated ability to work with large datasets and data pipelines, using tools such as pandas/data.table, SQL or similar databases. Experience in spatial statistics, extreme value analysis, or statistics applied to natural sciences is preferred, GIS or geospatial experience is a plus. #LI-BK2 2585571