Data Scientist Senior (Population Health)

Geisinger•Work from home (Pennsylvania), PA

7d•Remote

About The Position

The Senior Data Scientist is a strategic leader in our organization, driving the entire lifecycle of data science initiatives that directly impact healthcare outcomes. Leveraging your deep expertise and mastery of machine learning, you will spearhead the development, implementation, and evaluation of complex AI models in healthcare settings specifically population health. Your ability to translate technical concepts into actionable insights will empower stakeholders to make informed decisions that enhance patient care and operational efficiency. You will also play a crucial role in mentoring and developing junior data scientists and analysts, fostering a culture of data-driven innovation.

Requirements

Bachelor's Degree-Related Field of Study (Required)
Minimum of 4 years-Relevant experience (Required)
Analytical Thinking
C++ Programming Language
Clinical Data Cleaning
Communication
Group Collaboration
Machine Learning Methods
Python (Programming Language)
Statistical Methods
Structured Query Language (SQL)

Nice To Haves

Databricks
Python
SQL
advanced statistical analysis
machine learning
emerging AI technologies and implementation (LLMs, RAG, GenAI, Agentic workflow integrations and deployment)
Healthcare experience preferably with Population Health initiatives
Familiarity with Epic Clarity, Caboodle, claims data, CMS/Medicare populations, or payer-provider analytics

Responsibilities

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 healthcare research to identify opportunities for improvement and innovation.
Experiments with new approaches and technologies to enhance model performance and expand the organization's data science capabilities.
Work is typically performed in an office or remote environment.
Accountable for satisfying all job specific obligations and complying with all organization policies and procedures.