Principal Data Scientist

Overview The Principal Data Scientist is a senior leader and technical authority responsible for advancing Central Health System’s data science capabilities in support of population health, care management, and organizational decisionmaking. Operating under the general guidance of the VP of Data Insights & Innovation, this role leads and manages the organization’s Data Science team while serving as the primary data science authority within the organization, providing expert guidance on scientific rigor, validity, and equity of analytical and AI solutions. Working in close partnership with the Sr. Director of AI & Digital Innovation, the Principal Data Scientist provides critical technical input to the AI governance process, including risk assessments, model validation, efficacy adjudication, and alignment with frameworks such as the NIST AI Risk Management Framework (AI RMF). This role also establishes and enforces data science standards governing data quality, feature engineering, model documentation, and analytical reproducibility, ensuring that all data assets and methodologies used in AI and advanced analytics meet the organization's scientific and regulatory expectations. While the Sr. Director leads overall AI strategy, implementation and deployment, this role ensures that the underlying data science is sound, reproducible, ethical, and clinically meaningful. This individual will leverage the organization’s enterprise data environment, including Epic (EHR), VBA (TPA), Microsoft Azure (cloud infrastructure), Snowflake (cloud data platform), and numerous other data sources including clinical and business applications and our local health data utility (HDU formerly HIE), to develop and operationalize scalable, high-impact data science solutions. The Principal Data Scientist also serves as a senior technical advisor to Data Analyst teams, helping to oversee advanced analytics and ensuring advanced analytical deliverables meet the standards required to drive actionable insights across the organization. This position is considered Hybrid: Individuals in this position may work both at an approved off-site location and onsite at a primary location or multiple locations based on business needs. Responsibilities Essential Functions Data Science Team Leadership & People Management• Lead, manage, and develop a team of data scientists, providing day-to-day supervision, performance management, coaching, and professional growth planning.• Set clear team goals, priorities, and performance expectations aligned with organizational objectives, and hold team members accountable for quality, timeliness, and scientific rigor.• Recruit, onboard, and retain top data science talent, building a high-performing team with complementary skills across modeling, analytics, and MLOps.• Foster a collaborative, inclusive, and psychologically safe team culture that encourages innovation, intellectual curiosity, and continuous improvement.• Serve as the organization’s foremost technical expert in applied data science, statistical modeling, and machine learning as they relate to healthcare and population health.• Establish and maintain data science standards, methodologies, and best practices for model development, validation, documentation, and lifecycle management across the team.• Provide technical mentorship and direction to team members and data analysts, fostering a culture of scientific rigor and continuous learning.• Champion reproducible research practices, including version control of models, datasets, and analytical pipelines. Population Health & Care Management Modeling• Design, develop, and maintain predictive models and forecasting solutions that directly support population health management, care coordination, and chronic disease management programs.• Build and operationalize risk stratification models to identify high-risk patients and populations for proactive intervention by clinical and care management teams.• Develop disease progression models, readmission risk models, utilization forecasting, and other advanced analytics that inform care management and resource allocation strategies.• Leverage Epic clinical and operational data, including ADT events, clinical documentation, orders, and registry data, as primary source inputs for model development and validation.• Partner with the Clinical Informatics team to guide and inform predictive modeling efforts, ensuring models are grounded in clinical workflow context, aligned with care delivery priorities, and practically implementable at the point of care.• Collaborate with clinical, population health, and care management stakeholders to translate operational needs into well-defined data science problems with measurable outcomes.• Ensure all models are validated for accuracy, reliability, fairness, and clinical relevance before deployment, with ongoing monitoring for model drift and performance degradation. AI Governance & Risk Advisory• Partner with the Sr. Director of AI & Digital Innovation to provide expert data science input into the organization’s AI governance processes, policies, and committee structures.• Conduct technical evaluations of AI and machine learning tools under consideration for enterprise adoption, assessing scientific validity, algorithmic bias, data quality requirements, and clinical appropriateness.• Adjudicate the efficacy of AI solutions by reviewing vendor-provided evidence, internal pilot results, and published literature to inform go/no-go recommendations.• Apply knowledge of the NIST AI Risk Management Framework (AI RMF) and related frameworks (e.g., ISO/IEC 42001) to assess and document AI risk relative to organizational tolerance and regulatory requirements.• Identify and communicate potential risks associated with AI models, including bias, data drift, explainability gaps, and failure modes, ensuring the Sr. Director and governance committees have the scientific context needed for informed decision-m