What are the responsibilities and job description for the ML Scientist position at hackajob?
hackajob is collaborating with Verisk to connect them with exceptional professionals for this role.
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
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.
- 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.
Salary : $115,000 - $135,000