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About the Role

The Principal Data Scientist is a senior leader responsible for advancing Central Health System’s data science capabilities in population health, care management, and organizational decision-making. This role leads the Data Science team and serves as the primary data science authority, providing expert guidance on analytical and AI solutions.

Responsibilities

• Lead, manage, and develop a team of data scientists, providing supervision, performance management, coaching, and professional growth planning.
• Set clear team goals, priorities, and performance expectations aligned with organizational objectives.
• Recruit, onboard, and retain top data science talent, building a high-performing team.
• Foster a collaborative, inclusive, and psychologically safe team culture.
• Serve as the organization’s foremost technical expert in applied data science, statistical modeling, and machine learning.
• Establish and maintain data science standards, methodologies, and best practices for model development, validation, documentation, and lifecycle management.
• Provide technical mentorship and direction to team members and data analysts.
• Champion reproducible research practices, including version control of models, datasets, and analytical pipelines.
• Design, develop, and maintain predictive models and forecasting solutions for population health management, care coordination, and chronic disease management.
• Build and operationalize risk stratification models to identify high-risk patients for proactive intervention.
• Develop disease progression models, readmission risk models, utilization forecasting, and other advanced analytics.
• Leverage Epic clinical and operational data for model development and validation.
• Partner with the Clinical Informatics team to guide predictive modeling efforts.
• Collaborate with stakeholders to translate operational needs into data science problems.
• Ensure all models are validated for accuracy, reliability, fairness, and clinical relevance before deployment.
• 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 for enterprise adoption.
• Adjudicate the efficacy of AI solutions by reviewing evidence to inform recommendations.
• Apply knowledge of the NIST AI Risk Management Framework (AI RMF) to assess and document AI risk.
• Identify and communicate potential risks associated with AI models.
• Support the development and maintenance of model documentation, ensuring transparency and auditability.
• Contribute to the design and development of AI solutions, translating governance insights into actionable recommendations.
• Lead the design and execution of advanced analytics projects, including predictive modeling, machine learning, NLP, and time-series forecasting.
• Apply sophisticated statistical methods to complex healthcare data environments.
• Develop forecasting models for operational planning, including patient volume projections and staffing optimization.
• Ensure analyses account for the complexities of healthcare data, including missingness and bias.
• Translate analytical findings into clear, actionable insights communicated effectively to technical and non-technical audiences.
• Serve as the senior technical reviewer for advanced analytics work produced by Data Analyst teams.
• Define and maintain the boundary between standard reporting/analytics and advanced data science work.
• Collaborate with Data Analyst teams to build their statistical and analytical capabilities.
• Contribute to the development of a shared analytics environment built on Azure and Snowflake.
• Partner with data governance and data engineering teams to ensure data assets are accurate, complete, and well-documented.
• Actively identify and mitigate sources of bias in data and models, ensuring analytical and AI solutions promote health equity.
• Adhere to all applicable data privacy and security standards (HIPAA, etc.).
• Contribute to the development of the organization’s responsible AI and ethical data use policies.

Requirements

• 5 years of experience with applied data science, statistical modeling, or quantitative research experience post-PhD, with increasing responsibility and complexity.
• 3 years of experience in healthcare, public health, population health, or a similarly regulated and complex data environment.
• 2 years of demonstrated expertise in building, validating, and monitoring predictive models and machine learning solutions in a production or near-production environment.
• 3 years of experience developing models for population health, care management, risk stratification, or clinical decision support.
• 2 years of experience working with cloud-based data platforms such as Microsoft Azure and/or Snowflake for large-scale data science workflows.
• 3 years of experience directly managing or leading a team of data scientists or quantitative analysts, including hiring, performance management, and professional development.

Skills

• Data Science
• Statistical Modeling
• Machine Learning
• Population Health
• Care Management
• Predictive Modeling
• Risk Stratification
• AI Governance
• NIST AI Risk Management Framework (AI RMF)
• Natural Language Processing (NLP)
• Time-Series Forecasting
• Survival Analysis
• Mixed-Effects Models
• Bayesian Approaches
• Ensemble Methods
• Data Quality
• Data Governance
• Ethics
• Microsoft Azure
• Snowflake
• Epic (EHR)
• VBA (TPA)
• Data Engineering
• MLOps

Location

• Hybrid

Work Type

• Hybrid

Experience Level

• Principal
• Senior Leader

Education Level

• Doctoral or Professional Degree in Statistics, Biostatistics, Data Science, Epidemiology, Public Health Informatics, Computer Science, or related quantitative field

About the Company

• Central Health System is dedicated to advancing population health, care management, and organizational decision-making through data science.