Research Scientist Senior

Elevance Health•Indianapolis, IN

5d•$156,464 - $256,032•Hybrid

About The Position

The Research Scientist Senior is responsible for prospective and retrospective health economic research activities. Responsible for leadership in assessing scientific research that contributes to the transformation of health outcomes research to achieve a substantial national/global leadership. This role will focus on the design, development, evaluation, and deployment of advanced machine learning and reinforcement learning systems that optimize healthcare and operational decision-making. How you will make an impact: Leads the assessment of scientific research that contributes to the transformation of health outcomes research to achieve substantial national and global leadership. Acts as a technical authority within the organization and nationally/globally. Publishes finding and conclusions in recognized scientific publications and presents research at scientific conferences and forums. Leads projects that generate project extensions. Leads research project operations activities to include project conceptualization, project planning, leading project teams, and proposal defense presentations. Leads the proposal development process, project pricing, and project milestone forecasting. Ensures the quality and timeliness of deliverables to research clients. Serves as a hands-on individual contributor building scalable AI/ML solutions and production-grade decision intelligence systems in highly regulated healthcare environments. Develops scalable machine learning and reinforcement learning systems that improve healthcare outcomes, operational efficiency, and member experience through adaptive learning and advanced analytics. Demonstrates deep expertise in machine learning, reinforcement learning, predictive analytics, and intelligent decision systems. Applies strong research discipline and evidence-based methodologies to complex healthcare and operational problems. Successfully delivers scalable machine learning solutions from concept through deployment and measurable operational impact. Partners effectively across business, research, engineering, analytics, and product teams. Evaluates emerging AI technologies and translates innovation into scalable business value. Communicates highly technical concepts effectively to both technical and non-technical audiences. Leads prospective and retrospective health economic and outcomes research initiatives using healthcare administrative datasets including pharmacy and medical claims. Designs and develops machine learning, predictive modeling, and reinforcement learning solutions for healthcare optimization and intelligent decision systems. Builds and operationalizes production-grade ML systems. Partners closely with engineering, architecture, analytics, product, and business teams to deploy AI-driven recommendations and decision systems into operational workflows.

Requirements

PhD in health service research, biostatistics, epidemiology, health policy, economics or related field, or a clinical degree such as an MD or PharmD with residency or fellowship research training
A minimum of 10 years of experience conducting health outcomes research using health plan administrative databases, including pharmacy and medical claims data
A demonstrated record of peer-reviewed publications and/or presentations at scientific conferences
Any combination of education and experience which would provide an equivalent background

Nice To Haves

Hands-on expertise with Python and modern ML frameworks such as PyTorch, TensorFlow, Scikit-learn, or Ray highly preferred.
Experience building end-to-end machine learning systems and production AI infrastructure highly preferred.
Experience with reinforcement learning, predictive modeling, recommendation systems, or adaptive decision systems highly preferred.
Experience with cloud-native ML environments, distributed computing, and MLOps practices highly preferred.
Experience with healthcare AI, clinical decision support, or intelligent automation systems in regulated industries highly preferred.
Experience operationalizing Generative AI or LLM-enabled workflows preferred.
Managed care, PBM, pharmaceutical, or related experience preferred.

Responsibilities

Leads the assessment of scientific research that contributes to the transformation of health outcomes research to achieve substantial national and global leadership.
Acts as a technical authority within the organization and nationally/globally.
Publishes finding and conclusions in recognized scientific publications and presents research at scientific conferences and forums. Leads projects that generate project extensions.
Leads research project operations activities to include project conceptualization, project planning, leading project teams, and proposal defense presentations.
Leads the proposal development process, project pricing, and project milestone forecasting.
Ensures the quality and timeliness of deliverables to research clients.
Serves as a hands-on individual contributor building scalable AI/ML solutions and production-grade decision intelligence systems in highly regulated healthcare environments.
Develops scalable machine learning and reinforcement learning systems that improve healthcare outcomes, operational efficiency, and member experience through adaptive learning and advanced analytics.
Demonstrates deep expertise in machine learning, reinforcement learning, predictive analytics, and intelligent decision systems.
Applies strong research discipline and evidence-based methodologies to complex healthcare and operational problems.
Successfully delivers scalable machine learning solutions from concept through deployment and measurable operational impact.
Partners effectively across business, research, engineering, analytics, and product teams.
Evaluates emerging AI technologies and translates innovation into scalable business value.
Communicates highly technical concepts effectively to both technical and non-technical audiences.
Leads prospective and retrospective health economic and outcomes research initiatives using healthcare administrative datasets including pharmacy and medical claims.
Designs and develops machine learning, predictive modeling, and reinforcement learning solutions for healthcare optimization and intelligent decision systems.
Builds and operationalizes production-grade ML systems.
Partners closely with engineering, architecture, analytics, product, and business teams to deploy AI-driven recommendations and decision systems into operational workflows.