Penn Foster
Geospatial Statistician
Penn Foster, Boston, Massachusetts, us, 02298
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
Join the Mid-Latitude Atmospheric Perils Modeling Team in Verisk’s Boston Research Department and contribute to the next generation of probabilistic catastrophe models for assessing the risk of losses from weather extremes. Our team’s projects offer 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 the hierarchical modeling of physical space-time processes, inference from heterogeneous and incomplete calibration data, and ensemble techniques to quantify uncertainties. We learn continuously through interdisciplinary collaborations with a diverse team of statisticians, applied mathematicians, meteorologists, climate scientists, and engineers. As such, this is a fantastic position for a curious statistician with an interest in modeling atmospheric processes! In addition to possessing sharp technical skills, we also hope you will bring a practical, applied mindset to the role. Our team’s work contributes toward building tools that enable the (re)insurance sector to make sound decisions to enhance social resilience to natural catastrophes.About the Day-to-Day Responsibilities of the RoleDevelop code to represent characteristics of extreme weather events using observations, numerical weather models, and statistical models.Work with domain-area experts in atmospheric science to understand relevant datasets to translate model calibration and validation needs into appropriate statistical analyses and models.Analyze and validate Verisk’s stochastically generated storms against observational data to ensure statistical robustness. As part of a team, defend the statistical analysis on behalf of Verisk to external clients and reviewers.Contribute to identification, inference, and modeling of non-stationarities in atmospheric data, including those that may be due to climate change.Soundboard modeling problems with fellow team members working on other projects, and contribute to a team culture of mutual support, respect, and continued learning.
Qualifications
M.S. with 4+ years professional experience in a technical environment, orPh.D. degree in statistics, atmospheric science, or related disciplines.Advanced degree in statistics or equivalent statistical modeling experience required (PhD in statistics preferred; Master’s required.)Theoretical and applied command of spatial and/or spatio-temporal statistical modeling techniques.Fluency in R programming language is a necessity. Strength in writing robust, efficient, and well-organized code and packages under version control will be emphasized in selection of applicants.Programming in other languages (Python, Fortran, C, C++) as well as shell scripting and cluster computing would be a plus.Experience modeling atmospheric or climate system phenomena or interpreting statistical inference in terms of physical systems would be a significant advantage.Excellent written and verbal communication skills in both technical and non-technical contexts.Preference for early career scientists.
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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
Join the Mid-Latitude Atmospheric Perils Modeling Team in Verisk’s Boston Research Department and contribute to the next generation of probabilistic catastrophe models for assessing the risk of losses from weather extremes. Our team’s projects offer 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 the hierarchical modeling of physical space-time processes, inference from heterogeneous and incomplete calibration data, and ensemble techniques to quantify uncertainties. We learn continuously through interdisciplinary collaborations with a diverse team of statisticians, applied mathematicians, meteorologists, climate scientists, and engineers. As such, this is a fantastic position for a curious statistician with an interest in modeling atmospheric processes! In addition to possessing sharp technical skills, we also hope you will bring a practical, applied mindset to the role. Our team’s work contributes toward building tools that enable the (re)insurance sector to make sound decisions to enhance social resilience to natural catastrophes.About the Day-to-Day Responsibilities of the RoleDevelop code to represent characteristics of extreme weather events using observations, numerical weather models, and statistical models.Work with domain-area experts in atmospheric science to understand relevant datasets to translate model calibration and validation needs into appropriate statistical analyses and models.Analyze and validate Verisk’s stochastically generated storms against observational data to ensure statistical robustness. As part of a team, defend the statistical analysis on behalf of Verisk to external clients and reviewers.Contribute to identification, inference, and modeling of non-stationarities in atmospheric data, including those that may be due to climate change.Soundboard modeling problems with fellow team members working on other projects, and contribute to a team culture of mutual support, respect, and continued learning.
Qualifications
M.S. with 4+ years professional experience in a technical environment, orPh.D. degree in statistics, atmospheric science, or related disciplines.Advanced degree in statistics or equivalent statistical modeling experience required (PhD in statistics preferred; Master’s required.)Theoretical and applied command of spatial and/or spatio-temporal statistical modeling techniques.Fluency in R programming language is a necessity. Strength in writing robust, efficient, and well-organized code and packages under version control will be emphasized in selection of applicants.Programming in other languages (Python, Fortran, C, C++) as well as shell scripting and cluster computing would be a plus.Experience modeling atmospheric or climate system phenomena or interpreting statistical inference in terms of physical systems would be a significant advantage.Excellent written and verbal communication skills in both technical and non-technical contexts.Preference for early career scientists.
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