Actuarial Research Assistant - Math and Statistics Department

The Actuarial Research Assistant will support faculty research in actuarial science, risk sharing, and machine learning. The student will assist with data generation, numerical analysis, network-based risk-sharing models, and the application of statistical and machine-learning methods to evaluate and predict risk reduction. The position may also involve preparing figures, tables, and summaries of research results.

• Take initiative to learn new tools, systems, or procedures on the job.
• Present ideas or updates in a clear and organized manner.
• Identify when additional information is needed before making a decision.
• Break down tasks into steps to complete work more efficiently.
• Learn new software or digital platforms as needed for your role.

• Assist with faculty-led research in actuarial science, peer-to-peer insurance, and network-based risk sharing.
• Generate and organize numerical and simulated data for network-based risk-sharing models.
• Compute and summarize structural characteristics of networks and evaluate corresponding risk-sharing outcomes.
• Apply statistical and machine-learning methods, including regression, random forests, and boosting methods, to analyze and predict risk-reduction measures.
• Conduct numerical analysis and assist with model evaluation, validation, and interpretation of results.
• Use R, Python, or related computational tools to implement research methods and manage data.
• Prepare figures, tables, and concise summaries of research findings.
• Participate in regular meetings with the faculty supervisor, provide progress updates, and incorporate feedback into ongoing work.
• Maintain organized and reproducible research files, code, and documentation.
• Perform other research-related duties associated with the project as assigned.

• Currently enrolled as an undergraduate student in Actuarial Science at Bowling Green State University.
• Junior or senior standing preferred.
• Completion of, or substantial preparation equivalent to, MATH 4440, MATH 4260, and MATH 4270.
• Background in probability, statistics, actuarial mathematics, and quantitative analysis.
• Experience with R, Python, or another programming language used for statistical or numerical analysis.
• Familiarity with network-based risk sharing, peer-to-peer insurance, or related network models.
• Ability to work independently, follow research instructions, and meet established deadlines.
• Strong analytical, problem-solving, and communication skills.
• Willingness to learn and apply statistical and machine-learning methods as required by the research project.

• The student should be available to work on the project during Fall 2026 and participate in regular research meetings. Availability to assist with preparation for the Honda AI+X Final Showcase in January 2027 is preferred.
• Applicants should submit a resume and an unofficial transcript or list of completed relevant coursework.