Principal Data Scientist - Clinical Operations

At CVS Health, we’re building a world of health around every consumer and surrounding ourselves with dedicated colleagues who are passionate about transforming health care.

As the nation’s leading health solutions company, we reach millions of Americans through our local presence, digital channels and more than 300,000 purpose-driven colleagues – caring for people where, when and how they choose in a way that is uniquely more connected, more convenient and more compassionate. And we do it all with heart, each and every day.

Position Summary

Summary:

We are seeking a visionary and technically skilled Principal Data Scientist to lead the design and development of advanced AI/ML solutions that transform clinical operations at Aetna. This role will drive innovation across care management, utilization review, and government programs by embedding intelligent automation and predictive analytics into core workflows. The Principal Data Scientist extracts knowledge and insights from data to investigate complex business problems through a range of data preparation, modeling, analysis and/or visualization techniques, including predictive analysis, business intelligence, pattern recognition, operational effectiveness and/or economic forecasting.

Key skills include data analysis techniques (e.g., A/B testing, association rule learning, cluster analysis, pattern recognition and predictive modeling), data analysis and visualization software (e.g., Domo, Looker, PowerBI, Qlik, Tableau, SiSense), object-oriented programming languages (Java, Python, R, Ruby, Scala, C++) and/or data engineering skills (relational or NoSQL databases, ETL tools).

Key Responsibilities:

AI/ML Technical Design & Development

• Coordinates advanced data science projects and develops deliverables, taking ownership of assigned tasks and ensuring their successful completion.
• Drives data science strategies and methodologies for specific projects, applying domain knowledge and expertise to drive impactful outcomes.
• Develops methods for conducting risk assessments for data science initiatives, identifying potential vulnerabilities and proposing mitigation strategies.
• Conducts advanced analysis of the organization's data science needs, capabilities, and gaps to identify opportunities for improvement and provide recommendations for optimization.
• Plans major risk assessments for data science initiatives, identifying potential vulnerabilities and proposing mitigation strategies.
• Build and maintain AI/ML models for disease progression forecasting, care management optimization, and clinical appeals triage.
• Review modeling proposals and engineering designs to enhance efficiency and accuracy in clinical workflows.
• Develop advanced question-answering pipelines to support evidence-based decision-making by nurses and care advocates.
• Implement multi-agent LLM workflows for clinical document interpretation, outreach strategy recommendations, and appeal resolution.
• Customize LLMs to align with healthcare compliance (HIPAA, CMS), Aetna-specific terminology, and plan protocols.
• Define KPIs and monitoring systems to ensure safety, fairness, and longevity of AI solutions across clinical domains.
• Shape the AI roadmap for shared services such as appeals automation and digital care plans.
• Develop and implement generative AI use cases through proof-of-concepts and internal training materials.

Cross-Functional Collaboration & Innovation

• Advises senior management of multiple departments to contribute data-driven insights, expertise, and solutions to address healthcare challenges.
• Partner with stakeholders in clinical operations, government programs, and innovation to co-design impactful AI solutions.
• Applies advanced knowledge of data governance policies and procedures to ensure compliance, privacy, and ethical use of healthcare data.
• Promote responsible AI principles and empower clinical leaders to become AI change agents.
• Collaborate with care advocates, medical directors, engineers, and product managers to integrate AI into platforms like Clinical Gateway and HealthOS.
• Resolve platform-level challenges in real-time eligibility checks, provider documentation review, and predictive triage.
• Align AI designs with enterprise clinical strategy and IT architecture standards.
• Masters industry trends and emerging technologies, continuously enhancing skills and knowledge to contribute effectively as an independent contributor in the field of data science.

Required Qualifications

• 10+ years of experience in applied machine learning, preferably in healthcare.
• Proven track record of deploying AI/ML solutions in clinical or operational settings.
• Exp