Senior Model Validation Analyst

Bring your analytical curiosity and commitment to quality to a role where your work will help strengthen the natural catastrophe models organizations rely on to understand and manage complex risk. As a Senior Model Validation Analyst, you will independently evaluate sophisticated scientific, engineering, and financial model components, turning complex data into clear evidence that models are accurate, reliable, and ready to support critical decisions.

Working closely with scientists, structural engineers, software professionals, and other cross-functional partners, you will shape thoughtful validation strategies, develop creative ways to expand test coverage, and communicate findings that influence model quality across the organization. This is an opportunity to combine advanced analytics, technical problem-solving, and collaborative leadership in a highly visible role.

In this role, you will:

• You will lead the design and execution of comprehensive test plans from requirements, ensuring robust quality assurance coverage across complex natural catastrophe model components.
• You will independently validate scientific, engineering, and financial algorithms against original sources and established validation principles to support model accuracy and reliability.
• You will independently analyze and manipulate medium-to-large data sets, run advanced statistical analyses, and interpret results to identify trends, anomalies, and actionable insights.
• You will independently create transparent, reproducible technical documentation in Python’s Jupyter Notebook or R Markdown, capturing validation methods, evidence, findings, and conclusions.
• You will communicate validation results, risks, and recommendations clearly to technical and non-technical stakeholders through written reports and presentations.
• You will collaborate within cross-functional Agile Scrum teams, manage concurrent validation priorities, and support the expansion of automated testing throughout the software development life cycle.

• Candidates must have an undergraduate/graduate degree in STEM related areas (data science, engineering, science, mathematics, finance, economics) as well as a graduate degree and 4+ years relevant work experience.
• Candidates must have experience in analytical programming, fluency in languages like Python or R, DB experience such as SQL, knowledge of GIS tools, libraries like Pandas, Tidyverse, Data frames and Claude Code.
• Candidates must have experience working with large data sets performing analysis and manipulation.
• Experience with designing and/or validating numerical probabilistic models in engineering, science, catastrophe modeling, finance, actuarial science, etc.
• Candidates must have excellent attention to detail and experience with deriving actionable insights from data.
• Candidate must have excellent communication skills to interface with cross-functional teams.
• Prior experience with AWS ecosystem and technology stack is preferable.