Statistician, Atmospheric Extremes
- Employer
- Verisk
- Location
- Boston, Massachusetts, US
- Salary
- Competitive
- Closing date
- Dec 1, 2021
View more
- Sector
- Academic / Research
- Field
- Conservation science
- Discipline
- Climate Change
- Salary Type
- Salary
- Employment Type
- Full time
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Job Description
STATISTICIAN INATMOSPHERIC EXTREMES
Join the Atmospheric Perils Team in AIRs Boston-based Research Department and contribute to the development catastrophe models and probabilistic representation of atmospheric extreme events. Research projects offers opportunities for creative problem-solving on a boundless set of interesting statistical problems at the interface of extreme weather, climate change, and risk. Catastrophe models depend fundamentally on hierarchical modeling of space-time physical processes, inference from heterogeneous and incomplete calibration data, and use of ensemble techniques to quantify uncertainties. We learn continuously through interdisciplinary collaborations with a diverse team of meteorologists, applied mathematicians, climate scientists, engineers, and hydrologists. In addition to possessing sharp technical skills, we also hope you will bring a practical, applied mindset to the role. Our teams work contributes toward building tools that enable the (re)insurance sector to make sound decisions to enhance social resilience to natural catastrophes.
DAY TO DAY RESPONSIBILITIES
* Work with domain-area experts in meteorology, hydrology, and engineering to interpret observations and numerical simulations to generate statistical models of extreme events.
* Aid in the interpretation and maintenance of existing extreme event models.
* Develop innovative strategies in data analytics, workflows, and model formulation to accelerate production of the next generation of models and products.
* Analyze and validate AIRs extreme event catalog with recent observational data.
* Contribute to identification, inference, and modeling of significant non-stationarities within observational data, including those that may be due to climate change.
* Help formulate answers to client questions related to the statistical aspects of AIRs extreme event models.
STATISTICIAN INATMOSPHERIC EXTREMES
Join the Atmospheric Perils Team in AIRs Boston-based Research Department and contribute to the development catastrophe models and probabilistic representation of atmospheric extreme events. Research projects offers opportunities for creative problem-solving on a boundless set of interesting statistical problems at the interface of extreme weather, climate change, and risk. Catastrophe models depend fundamentally on hierarchical modeling of space-time physical processes, inference from heterogeneous and incomplete calibration data, and use of ensemble techniques to quantify uncertainties. We learn continuously through interdisciplinary collaborations with a diverse team of meteorologists, applied mathematicians, climate scientists, engineers, and hydrologists. In addition to possessing sharp technical skills, we also hope you will bring a practical, applied mindset to the role. Our teams work contributes toward building tools that enable the (re)insurance sector to make sound decisions to enhance social resilience to natural catastrophes.
DAY TO DAY RESPONSIBILITIES
* Work with domain-area experts in meteorology, hydrology, and engineering to interpret observations and numerical simulations to generate statistical models of extreme events.
* Aid in the interpretation and maintenance of existing extreme event models.
* Develop innovative strategies in data analytics, workflows, and model formulation to accelerate production of the next generation of models and products.
* Analyze and validate AIRs extreme event catalog with recent observational data.
* Contribute to identification, inference, and modeling of significant non-stationarities within observational data, including those that may be due to climate change.
* Help formulate answers to client questions related to the statistical aspects of AIRs extreme event models.
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