ML Scientist
Maplecroft, Boston, MA, United States
Job Description
Join Verisk Catastrophe and Risk Solutions and contribute to the development of cutting‑edge machine learning models for climate risk. You will play a critical role in expanding our modeling capabilities to improve predictions of weather extremes and enhance the risk insights we deliver to clients. In this role, you will be responsible for building, deploying, and validating machine learning algorithms to solve real‑world climate problems, contributing to the development of stochastic event catalogs for perils such as winter storms, storm surge, tropical cyclones, and other natural hazards. This role is a perfect blend between machine learning and climate science, and your work will directly impact how insurers, reinsurers, and governments prepare for and respond to catastrophic events.
Responsibilities
Design, build, deploy, and maintain ML models to achieve research and business objectives.
Collaborate with and provide support to other research groups to develop new machine learning tools and enhance existing models.
Evaluate model performance on real‑world data and present findings to stakeholders.
Prepare, clean, process, and quality‑control data used in modeling workflows.
Collaborate with meteorologists, hydrologists, and engineers to integrate physical insights into ML models.
Qualifications
Ph.D. degree (completed or close to completion) in computer science, statistics, physics, engineering, atmospheric sciences, or a related field.
Strong background in machine learning for spatio‑temporal data and physical processes.
Theoretical understanding of fluid dynamics and climate processes.
Proficiency in Python and ML libraries (such as PyTorch or Jax) as well as version control (Git).
Experience with climate data (e.g., reanalysis, satellite, or numerical weather prediction outputs).
Familiarity with geospatial data processing and visualization tools (e.g., xarray, NetCDF, pandas).
High degree of comfort deploying machine learning models in HPC environments.
Excellent verbal and written communication skills, including the ability to convey technical ideas to a non‑technical audience.
Team‑focused and evidence of supporting project team members.
Benefits
Verisk invests in a benefits package for all employees that includes the following: Health Insurance, a Retirement Plan, Disability benefits, and a Paid Time Off program. We offer a competitive total rewards package that includes base salary determined based on role, experience, skill set, and location.
All members of the Verisk Analytics family of companies are equal opportunity employers. We consider all qualified applicants for employment without regard to race, religion, color, national origin, citizenship, sex, gender identity and/or expression, sexual orientation, veteran's status, age or disability. Verisk’s minimum hiring age is 18 except in countries with a higher age limit subject to applicable law.
#J-18808-Ljbffr
Join Verisk Catastrophe and Risk Solutions and contribute to the development of cutting‑edge machine learning models for climate risk. You will play a critical role in expanding our modeling capabilities to improve predictions of weather extremes and enhance the risk insights we deliver to clients. In this role, you will be responsible for building, deploying, and validating machine learning algorithms to solve real‑world climate problems, contributing to the development of stochastic event catalogs for perils such as winter storms, storm surge, tropical cyclones, and other natural hazards. This role is a perfect blend between machine learning and climate science, and your work will directly impact how insurers, reinsurers, and governments prepare for and respond to catastrophic events.
Responsibilities
Design, build, deploy, and maintain ML models to achieve research and business objectives.
Collaborate with and provide support to other research groups to develop new machine learning tools and enhance existing models.
Evaluate model performance on real‑world data and present findings to stakeholders.
Prepare, clean, process, and quality‑control data used in modeling workflows.
Collaborate with meteorologists, hydrologists, and engineers to integrate physical insights into ML models.
Qualifications
Ph.D. degree (completed or close to completion) in computer science, statistics, physics, engineering, atmospheric sciences, or a related field.
Strong background in machine learning for spatio‑temporal data and physical processes.
Theoretical understanding of fluid dynamics and climate processes.
Proficiency in Python and ML libraries (such as PyTorch or Jax) as well as version control (Git).
Experience with climate data (e.g., reanalysis, satellite, or numerical weather prediction outputs).
Familiarity with geospatial data processing and visualization tools (e.g., xarray, NetCDF, pandas).
High degree of comfort deploying machine learning models in HPC environments.
Excellent verbal and written communication skills, including the ability to convey technical ideas to a non‑technical audience.
Team‑focused and evidence of supporting project team members.
Benefits
Verisk invests in a benefits package for all employees that includes the following: Health Insurance, a Retirement Plan, Disability benefits, and a Paid Time Off program. We offer a competitive total rewards package that includes base salary determined based on role, experience, skill set, and location.
All members of the Verisk Analytics family of companies are equal opportunity employers. We consider all qualified applicants for employment without regard to race, religion, color, national origin, citizenship, sex, gender identity and/or expression, sexual orientation, veteran's status, age or disability. Verisk’s minimum hiring age is 18 except in countries with a higher age limit subject to applicable law.
#J-18808-Ljbffr