FM is strategically investing in global climate research to offer unparalleled loss preparedness and prevention solutions due to increasing challenges posed by climate risk in the coming decades.
Requirements
- Proficient in collecting, processing, and analyzing large datasets.
- Proficiency in statistical methods, including extreme value analysis and probabilistic modeling.
- Strong background in data science techniques, including machine learning (ML), neural networks and artificial intelligence (AI) methods with proven experience in developing machine learning models applied to geospatial or climate data.
- Understanding of meteorological processes, particularly related to tropical cyclones, severe convective storms, synoptic-scale systems, and/ or extreme rainfall.
- Demonstrated experience using or developing global and/or regional climate models, analyzing CMIP6 global climate model output, and combining large atmospheric datasets in various formats (including GeoTIFF, NetCDF, HDF, and GRIB).
- Experience writing shell scripts and using APIs for process automation, excellent programming skills in at least two programming languages including Python, R, Fortran, and/or Matlab.
- Proficiency in utilizing Linux/Unix clusters for high performance computing, along with experience in DevOps practices and cloud platforms such as AWS and Azure.
Responsibilities
- Identify, plan, and conduct innovative research projects focused on extreme weather events.
- Develop and implement novel techniques and models to enhance risk analytics and loss prevention.
- Contribute to the development of hazard models assessing flood, wind, storms, and wildfire risks.
- Modeling the evolving risks and impacts of phenomena such as tropical cyclones, severe convective storms, and extreme precipitation.
- Support regional research initiatives and contribute to our global Climate Risk & Resilience activities.
Other
- MS in meteorology, data science or an adjacent field, with practical experience in the modelling of extreme weather, plus more than 3 years of professional experience with a good working knowledge of statistical/ dynamical downscaling techniques.
- PhD in meteorology, data science or an adjacent field with a good working knowledge of statistical/ dynamical downscaling techniques.
- Proven ability to manage projects and conduct research effectively.
- Collaborate with a global team of scientists across the U.S., Singapore, and Luxembourg.
- Excellent oral and written communication and presentation skills.
