Principal Data Scientist

Essential Functions

• 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.
• 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. Serve 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.

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-making. Support the development and maintenance of model documentation, including model cards, data lineage, and fairness assessments, ensuring transparency and auditability.

Predictive Analytics & Advanced Statistical Analysis

• Lead the design and execution of advanced analytics projects, including predictive modeling, machine learning, natural language processing (NLP) for clinical text, and time-series forecasting. Apply sophisticated statistical methods, including survival analysis, mixed-effects models, Bayesian approaches, and ensemble methods, to complex healthcare data environments. Develop forecasting models to support operational planning, including patient volume projections, staffing optimization, and financial performance indicators. Ensure analyses account for the complexities of healthcare data, including missingness, selection bias, confounding, and longitudinal follow-up. Translate analytical findings into clear, actionable insights communicated effectively to both technical and non-technical audiences.

Data Quality, Governance & Ethics

• Partner with data governance and data engineering teams to ensure that data assets used for modeling and analytics are accurate, complete, well-documented, and governed appropriately. Actively identify and mitigate sources of bias in data and models, ensuring that analytical and AI solutions promote health equity and do not exacerbate disparate outcomes. Adhere to all applicable data privacy and security standards (HIPAA, etc.) in the collection, use, and storage of data for analytical purposes. Contribute to the development of the organization’s responsible AI and ethical data use policies, ensuring scientific perspectives are well-represented.