Senior Research Engineer 1

Catastrophe Risk Solutions at Verisk (formerly AIR Worldwide) is the scientific leader and most respected provider of risk modeling software and consulting services. It founded the catastrophe modeling industry in 1987 and today models the risk from natural catastrophes, terrorism, and pandemic outbreaks in 90 countries. Our Atmospheric Perils Vulnerability team develops, maintains, and researches the relationship between atmospheric hazards—such as hurricanes, severe convective storms, and winter storms—and the built environment. We are a diverse group of engineers and research scientists with backgrounds in wind, flood, earthquake, statistics, and data science, focused on building cutting-edge vulnerability models and shaping the future of risk management. Team members bring their unique backgrounds and expertise in civil engineering and statistics to model extreme catastrophic events (e.g., hurricanes, tornadoes, storm surge, and flood) and assess building performance during these events, while working with advanced technologies, global clients, and some of the most innovative minds in the industry. The evolving nature of research at Verisk creates unique, challenging problems that spark innovation and create opportunities for growth. A successful candidate will enjoy applying sound engineering principles and strong problem-solving skills to these problems in civil/structural engineering and risk assessment. We are looking for someone who is highly motivated, detail-oriented, well-organized, and team-oriented, who can conduct high-quality self-directed research and has outstanding written and verbal communication skills. Must Haves: Non-negotiable skills/experience the candidate needs to succeed from day one. Ph.D. or M.S. in Civil Engineering with an emphasis in Structural Engineering or Building Sciences 2+ years of Catastrophe Modeling or damage estimation experience Excellent written and verbal communication skills Experience in performance-based design, probabilistic and stochastic risk assessment and modeling, and reliability theory as applied to structural engineering Experience with C#, Python, or similar programming languages (e.g., C/C++, R, MATLAB), or experience implementing AI-assisted coding and familiarity with coding; proficiency in Microsoft Office Experience in data analytics, including analyzing, interpreting, and visualizing large datasets to support research and modeling Strong organizational and documentation skills Nice-to-Haves: Skills that are a plus but can be learned on the job — don't let these gate an otherwise strong candidate. Knowledge of GIS applications (e.g., ArcGIS) and SQL Server Experience in claims analysis, risk assessment, or forensic engineering #LI-BH1