Senior Scientist, Weather Research/Property Risk

Verisk is seeking a Senior Scientist I with a strong background in atmospheric science (or related field) and scientific programming skills to join our Weather Solutions team.

This team develops and maintains cutting-edge products and services that help clients in insurance, restoration, and related industries respond effectively to natural catastrophe events.

The ideal candidate will bring a combination of research expertise, technical skills, and collaborative mindset.

You will advance scientific products through research and development, work closely with engineers to move algorithms into production, and contribute to innovations that have a direct impact on how industries respond to severe weather.

Key Responsibilities * Conduct applied research in atmospheric science with focus on advancing Verisk's weather solutions.

* Develop and implement algorithms using observational and model-based meteorological data.

* Draft clear documentation for research methods, models, and algorithms.

* Collaborate with scientists, product managers, and software engineers to transition research into scalable products.

* Analyze and apply knowledge in one or more specialized domains such as severe convective weather, weather radar, weather modeling, hurricanes, or data assimilation.

* Contribute to the scientific vision of the team while ensuring product quality, relevance, and timeliness.

Required Qualifications * Advanced degree (MS or PhD) in Atmospheric Science, Meteorology, or related field, or a minimum of 2 years of relevant work experience.

* 2-5 years of programming experience in Python and/or C++ (preferably in a geospatial research setting). * Hands-on experience with meteorological observational and weather model data.

* Strong scientific coding background, specifically with geospatial data analysis and algorithm development.

* Demonstrated ability to conduct research and produce technical documentation.

Preferred Qualifications * Knowledge of severe convective weather processes.

* Experience with machine learning/AI techniques applied to atmospheric data.

* Familiarity with cloud computing platforms.

* Experience with software version control systems (e.g., Git). * Proficiency with Linux/UNIX.

#LI-SM1 #LI-Remote