Actuarial Analyst III

Elevance Health•Atlanta, IN

1d•Hybrid

About The Position

The Actuarial Analyst III is responsible for completing diverse and complicated projects and performing complex actuarial studies. This position will support the Commercial Actuarial Pricing Modeling Team.

Requirements

Requires a BA/BS and to have passed a minimum of four Society of Actuaries (SOA) actuarial exams and a minimum of 3 years related experience; or any combination of education and experience, which would provide an equivalent background.

Nice To Haves

Strong actuarial and analytical foundation with experience in healthcare pricing, underwriting, or actuarial modeling (commercial medical and pharmacy preferred).
Advanced Excel proficiency required; experience with VBA or other automation techniques strongly preferred.
Programming and data skills (e.g., SAS, SQL, R, Python or other tools) with experience in data extraction, manipulation, and large dataset analysis desired.
Demonstrated ability to perform rigorous data validation and apply sound actuarial judgment to ensure model integrity.
Knowledge of health plan design, benefit structures, and their impact on utilization and cost trends.
Strong communication and collaboration skills, with the ability to work independently while effectively partnering across teams.

Responsibilities

Support development, enhancement, and maintenance of the commercial medical and pharmacy benefit relativity model (AMEBA), a critical tool for pricing, underwriting, and group-level rating decisions.
Perform advanced data analysis, statistical modeling, and validation to ensure accuracy, reasonableness, and regulatory compliance of pricing outputs.
Translate complex plan design features into actionable insights by assessing their impact on cost, utilization, and consumer behavior.
Partner closely with cross-functional stakeholders—including Commercial Pricing, Underwriting, Product, and Rating Systems teams—to inform pricing strategy and business decisions.
Drive continuous improvement through automation and process streamlining initiatives to enhance model efficiency and scalability.
Communicate model methodology, results, and implications clearly to both technical and non-technical stakeholders; support training and adoption among end users.