Probabilistic Hazard Information (PHI) and AI/ML Research Specialist (Subject Matter Expert II)

FedWriters, Inc.•,

69d•$102,000 - $108,000•Remote

About The Position

Rapidly updating, probabilistic severe weather guidance is only as good as the science and technology behind it. As FWI's PHI and AI/ML Research Specialist supporting NSSL, you will apply expert, highly specialized knowledge to advance Probabilistic Hazard Information (PHI) systems by integrating Warn-on-Forecast (WoFS) ensemble guidance, meteorological observations, and machine learning techniques into next-generation short-term hazard prediction tools. Your work will bridge research and operations — from designing AI/ML methodologies to leading Hazardous Weather Testbed (HWT) engagement with real NWS forecasters — to make severe weather guidance faster, smarter, and easier to act on, while providing technical guidance and limited supervision to project team members.

Requirements

BS Degree (Advanced Degree Preferred) in meteorology, atmospheric science, computer science, data science, engineering, or a related field
8+ years of professional or academic experience
5 years of AI Experience preferred
Proficiency in scientific programming and software development using Python and related data analysis and visualization tools
Experience working with large observational and model datasets, including radar, satellite, lightning, and environmental observations
Familiarity with forecast verification methodologies, uncertainty quantification, and model evaluation techniques
Experience utilizing high-performance computing (HPC) resources and modern software development practices
Ability to communicate complex scientific concepts through presentations, technical reports, and peer-reviewed publications
Experience collaborating with operational users, stakeholders, or forecasters in the development and evaluation of decision-support tools

Nice To Haves

Experience conducting research involving severe convective storms, hazardous weather forecasting, or probabilistic prediction systems
Knowledge of ensemble forecasting systems, including Warn-on-Forecast (WoFS) or similar convection-allowing ensemble prediction systems
Experience applying artificial intelligence and/or machine learning techniques to meteorological, environmental, or geospatial datasets
Demonstrated experience leading a project team or providing limited supervision on a technical research effort

Responsibilities

Apply expert, specialized knowledge to advance PHI research and development activities that improve rapidly updating probabilistic forecasts of severe weather hazards
Design, develop, and evaluate AI/ML methodologies that enhance the generation, interpretation, and communication of probabilistic hazard forecasts
Lead development and implementation of tools that integrate WoFS ensemble output with PHI systems and workflows, providing technical guidance to team members
Develop methods to merge WoFS forecasts with meteorological observations (radar, satellite, lightning, environmental datasets) to create enhanced short-term forecast guidance products
Conduct data analysis, visualization, verification, and explainability studies to evaluate forecast performance and improve user understanding of AI-assisted forecast guidance
Direct development of software tools, workflows, and visualization products to support research, testing, and operational transition activities
Lead planning, facilitation, and analysis of user engagement activities conducted through the Hazardous Weather Testbed (HWT) and related evaluation programs
Analyze feedback from forecasters and other stakeholders to guide future development and refinement of PHI and WoFS-based products
Collaborate with scientists, developers, forecasters, and external partners to advance next-generation severe weather forecasting capabilities
Prepare technical documentation, software repositories, evaluation reports, and project deliverables supporting research and operational objectives
Contribute to quarterly and annual project reports
Lead or contribute to peer-reviewed publications and present research findings at scientific conferences, workshops, and stakeholder meetings