Milliman's San Francisco Tri-State Property and Casualty practice needs to develop sophisticated analytical models and solutions to process and visualize large, complex datasets, build risk scores, and develop predictive models for client engagements and internal research, with a focus on emerging risks like climate change and natural disasters.
Requirements
- Proficiency in programming languages such as SAS, R, or Python for data manipulation, analysis, and modeling.
- Understanding of relational database concepts and data management practices.
Responsibilities
- Manipulate, summarize, and validate large datasets to ensure accuracy and consistency using statistical programming languages.
- Build risk scores utilizing geospatial, climate, and contextual data to enhance predictive accuracy.
- Develop and refine predictive models employing linear and non-linear techniques to support insurance risk assessment.
- Contribute to ETL processes supporting client projects, product development, and internal research initiatives.
- Participate in peer reviews and quality checks of analytical work to maintain high standards.
- Assist project managers and colleagues in scoping new projects and identifying opportunities for data science applications.
- Draft clear, professional reports and visual exhibits tailored for internal stakeholders and external clients.
Other
- Minimum of one year of relevant professional experience in data-driven roles, preferably within property and casualty insurance, natural catastrophe modeling, climate risk, or related sectors.
- Willingness and ability to obtain and maintain a U.S. Government-issued Public Trust security clearance.
- Legal authorization to work in the United States without sponsorship or immigration support.
- Strong expertise with MS Office suite including Excel, PowerPoint, Teams, Outlook, and Word.
- Collaborate with cross-functional teams, contribute to the development of new methodologies, and communicate insights through professional reports and presentations.
