Support the development of innovative analytics and AI solutions that uncover early signals in cost trends across Medicaid, Medicare, Local Group, and Individual markets.
Collaborate with actuaries, finance leaders, and business stakeholders to enhance decision-making through advanced modeling, automation, and insight delivery.
Modernize how emerging cost drivers are detected using real-time data, AI-powered insights, and dynamic tools to support critical decisions for executive, actuarial, and financial audiences.
Develop and implement predictive models, trend detection algorithms, and statistical analyses using weekly claims, pharmacy, lab, and authorization data.
Apply machine learning and AI techniques to uncover leading indicators of health utilization trends and improve predictive accuracy of actuarial and financial models.
Build and enhance interactive dashboards and executive tools that translate complex data into actionable insights for business and finance leaders.
Design and implement scalable machine learning models and LLM-based solutions.
Apply NLP techniques and prompt engineering to build conversational and generative AI systems.
Develop APIs using FastAPI or Flask for model serving and integration.
Support the evolution of early-warning systems and automation for trend monitoring and reporting across multiple lines of business.
Translate model outputs and statistical insights into clear business narratives that inform planning, pricing, and strategic actions.
Requirements
Requires a Bachelor’s degree in Statistics, Computer Science, Mathematics, Machine Learning, Econometrics, Physics, Biostatistics or related Quantitative disciplines and 1 or more years’ experience in predictive analytics or equivalent; or any combination of education and experience, which would provide an equivalent background.
Prior experience in healthcare analytics or trend analysis is a strong advantage.
Proven ability to communicate technical findings and complex concepts to a non-technical audience and develop compelling visual narratives.
Proficiency with Python, R, SQL, or similar tools to manipulate large, complex healthcare data sets.
Experience with data visualization platforms (Power BI, Tableau, or equivalent).