Senior Model Validation Analyst

We are looking for a Senior Model Validation Analyst to join Verisk's Extreme Event Solutions (EES) team. In this role, you will be responsible for validating catastrophe risk models by ensuring the accuracy, reliability, and quality of scientific, engineering, and financial algorithms. You will collaborate closely with scientists, structural engineers, and cross-functional Agile teams to design comprehensive validation strategies, analyze large datasets, and develop automated testing solutions using Python or R.

• Validate catastrophe and extreme event risk models to ensure accuracy, quality, and reliability.
• Design and execute comprehensive validation strategies, test plans, and automated testing approaches.
• Perform advanced statistical analysis on large datasets to validate complex model components.
• Identify validation gaps and develop innovative approaches to improve model test coverage.
• Collaborate with scientists, structural engineers, software engineers, and product teams throughout the model development lifecycle.
• Analyze and transform data using Python or R and SQL to support validation activities.
• Document validation methodologies, findings, and recommendations using Jupyter Notebook or R Markdown.
• Ensure scientific, engineering, and financial algorithms meet functional and quality standards.
• Participate in Agile Scrum ceremonies and contribute to continuous improvement of QA and validation processes.
• Present validation results and technical findings to both technical and non-technical stakeholders.

• Master's degree (or higher) in a STEM field such as Data Science, Engineering, Mathematics, Statistics, Computer Science, Finance, Economics, or related discipline.
• 4+ years of experience in model validation, quantitative analytics, data science, quality assurance, or a related analytical role.
• Strong programming skills in Python or R.
• Experience working with SQL and large-scale datasets.
• Hands-on experience with data analysis libraries such as Pandas, Tidyverse, and DataFrames.
• Experience designing, validating, or testing numerical or probabilistic models in areas such as engineering, catastrophe modeling, finance, actuarial science, or scientific research.
• Strong statistical analysis and data manipulation skills.
• Excellent analytical thinking with exceptional attention to detail.
• Strong written and verbal communication skills with the ability to collaborate across cross-functional teams.