Statistical Modeler in Wildfire Team - Prague, Czech Republic v Brno-Vinohrady, Česko

Statistical Modeler in Wildfire Team - Prague, Czech Republic

Statistical Modeler in Wildfire Team

We are looking for a Statistician to join our Impact Forecasting Wildfire team. This is a handson 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 st atistical and machine learning models using Python, R , or a combination of both, applying modern development practices and tools (Git, AIassisted 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 clientfacing 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, youll work with colleagues around the world in an environment that values integrity, authenticity, and a cando approach. We offer flexible working, home office options, and an attractive benefits package including pension contributions, inoffice massages, language courses, ergonomic desks, sick days, and more.

Skills and Experience 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, AIassisted 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.

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