Mid-Level
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
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Contribute to the design, analysis, and validation of models, ensuring methods and outputs are robust and transparent.
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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).
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Handle large and complex datasets, including cleaning, feature engineering, exploration, and diagnostics.
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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.
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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.
Skills and Experience That Will Lead to Success
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University degree in statistics, applied mathematics, or a related field, with a solid base in probability, inference, and statistical modeling and relevant working experience
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Proficient programming skills in Python and/or R, writing efficient, readable code and using modern tooling (e.g., Git, GitHub, AI-assisted coding tools).
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Experience with modern machine learning methods and their appropriate use and limitations.
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Demonstrated ability to work with large datasets and data pipelines, using tools such as pandas/data.table, SQL or similar databases.
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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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