Data Scientist Senior Population Health

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Job Summary:

Job Duties:

• Leads and manages the entire lifecycle of data science projects, from conceptualization and design to development, deployment, and ongoing optimization.
• Build and deploy advanced analytics that explain and predict acute utilization (Inpatient/Emergency Department) and quantify how care delivery changes (e.g., panel shifts, capacity differences, continuity disruption) impact outcomes for heart failure and other high-risk populations.
• Translate longitudinal patient care data into actionable intervention points across primary care, specialty care, and monitoring programs.
• Partner with clinical and operational leaders to convert analytic findings into care pathway recommendations, operational triggers, and monitoring protocols; define measures of success and evaluate impact.
• Collaborate with cross-functional teams to define project scope, objectives, analytic design, validation strategy, and expected impact, ensuring alignment with organizational goals and measurable improvements in healthcare outcomes.
• Leverages deep understanding of machine learning algorithms to build patient-level and population-level models that support risk stratification, trajectory analysis, forecasting, capacity planning, and scenario analysis for diverse healthcare applications.
• Utilizes clustering, dimension reduction, and deep generative models to uncover hidden patterns and insights within large, complex healthcare datasets.
• Applies rigorous validation techniques to ensure model accuracy, stability, fairness, generalizability, and clinical usefulness across patient cohorts, sites, time periods, and operational settings.
• Oversees the deployment of models into production environments, ensuring seamless integration with existing systems.
• Extracts insights from clinical and operational data sources (Epic Clarity, HL7, and other enterprise data sources) to inform decision-making and guide project direction.
• Translates complex technical findings into compelling narratives that resonate with non-technical stakeholders through presentations, dashboards, technical documentation, and stakeholder discussions.
• Facilitates data-driven decision-making by effectively communicating the value and impact of AI models.
• Mentors and guide junior data scientists, fostering their professional growth and technical expertise.
• Promotes a culture of collaboration, knowledge sharing, and continuous learning within the data science team.
• Contributes to developing best practices and standards for data science and machine learning within the organization.
• Stays abreast of the latest advancements in machine learning and hea