Actuarial Research Assistant - Math and Statistics Department

Bowling Green State University•Main Campus (BG), OH

About The Position

The Actuarial Research Assistant supports faculty research in actuarial science, risk sharing, and machine learning. The student assists with data generation, numerical analysis, network-based risk-sharing models, and the use of statistical and machine-learning methods to evaluate and predict risk reduction. The work may also include preparing figures, tables, and summaries of research results. The student is expected to participate in regular meetings with the faculty supervisor, provide progress updates, incorporate feedback, and maintain organized, reproducible research files, code, and documentation. The student should be available during Fall 2026. Availability to assist with preparation for the Honda AI+X Final Showcase in January 2027 is preferred.

Requirements

Currently enrolled as an undergraduate student in Actuarial Science at Bowling Green State University.
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.
Availability to work on the project during Fall 2026.
Applicants should submit a resume and an unofficial transcript or list of completed relevant coursework.

Nice To Haves

Junior or senior standing is preferred.
Availability to assist with preparation for the Honda AI+X Final Showcase in January 2027 is preferred.

Responsibilities

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 network structural characteristics 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.