Company Description
We help the world see new possibilities and inspire change for better tomorrows. Our analytic solutions bridge content, data, and analytics to help business, people, and society become stronger, more resilient, and sustainable.
Job Description
About the Role
Make a meaningful impact in the world with wildfire modeling. Extreme fires in recent years have caused billions of dollars in properties lost and damaged. In addition, in a changing climate, the historical patterns of wildfire activity are also changing and challenging the ability of the insurance industry to mitigate the risk. Wildfire modeling seeks to quantify the risk, allowing the industry to better prepare for disaster before it strikes.
Join our growing Research and Modeling department as a member of our wildfire modeling team. You will work with the team to develop, optimize, and deploy advanced computational models that simulate fire ignition, spread, and penetration into the wildland-urban interface at meter-scale resolution for millions of scenarios, and that describe the complex relationships between wildfire activity, weather/climate, fuels, and other factors. As a member of this collegial team, you will work closely with a range of stakeholders to (a) incorporate up-to-date scientific knowledge, data, and modeling approaches into our existing models; (b) develop new models and model components; and (c) deploy existing modeling technologies into new regions and/or for new applications.
About the Day to Day Responsibilities of the Role
- Keep abreast of current wildland fire research and look for ways to integrate this research into existing and new wildfire models.
- Understand the principles of wildland fire models and how their components work together to assess sensitivity to key drivers (e.g., fuel type, weather).
- Critically assess and estimate statistical significance of trends in wildfire activity and climate drivers in the United States and elsewhere.
- Evaluate new climatological, wildfire fuel, and wildfire activity; integrate into existing models; and explain changes seen in modeled results.
- Create, optimize, and run critical model code using the principles of high-performance computing.
- Navigate and manage multiple code repositories, data sets, and data archives with appropriate approaches to archiving and version control.
- Prepare accurate and effective technical and non-technical documents describing modeling processes and results, both for client use and internal model improvement.
- Create and deliver presentations to coworkers, company management, and clients.
- Deliver results on time to meet project objectives.
Qualifications
About You and How You Can Excel in This Role
- Solid foundational knowledge of atmospheric, climatic and/or hydrological processes and their quantification across large landscapes.
- Practical experience analyzing/interpreting large model output datasets for both model evaluation and improvement.
- Mathematical and/or statistical experience and practical experience developing new models and/or significant new components (e.g., advanced subroutines) in existing models.
- Programming skills gained through practical experience with a modern programming languages is essential.
- Ability to gather, understand, and critically analyze information from all relevant sources, such as historical fire/weather/climate databases, scientific papers, government reports, insurance data, etc.
- Demonstrated ability to learn new concepts and skills and to meet project objectives and deadlines.
- Excellent written and oral communication skills.
- Knowledge of the application of probabilistic modeling to (re)insurance applications and/or climate change science a plus.
- Experience applying climate and/or weather forecasting model output to landscape processes is a plus.
- Experience using and managing large datasets; practical experience with SQL is desirable.
- Experience with ArcGIS a plus.
- Advanced degree in wildfire, climate, atmospheric or environmental science, engineering, mathematics, or statistics (Ph.D. preferred).