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

Overview 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.

Work Schedule and Location: Remote This full-time remote position will work Monday through Friday, 8 AM CST to 5 PM CST.

FWI is expanding rapidly and has been recognized as a 2024, 2025 and 2026 Top Workplace, offering excellent growth opportunities in a collaborative environment .

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

Required Qualifications

• 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)

Desired

• 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
• 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

Work Setting And Environment

• Primary…