Data Scientist Senior (Population Health) at Geisinger Clinic

, Pennsylvania, United States -

Full Time

Start Date

Immediate

Expiry Date

28 Aug, 26

Salary

0.0

Posted On

30 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Databricks, Python, SQL, Advanced Statistical Analysis, Machine Learning, LLMs, RAG, GenAI, Agentic Workflow, Population Health, Epic Clarity, Caboodle, C++, Clinical Data Cleaning, Analytical Thinking, Communication

Industry

Hospitals and Health Care

Description

Location: Work from home (Pennsylvania) Shift: Days (United States of America) Scheduled Weekly Hours: 40 Worker Type: Regular Exemption Status: Yes Job Summary: 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. 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 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. The specific statements in this profile are not intended to be all-inclusive. They represent typical elements considered necessary to successfully perform the job. *Relevant experience may be a combination of related work experience and degree obtained (Master's Degree = 2 years; PHD = 4 years ). Position Details: Preferred skills: 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 Education: Bachelor's Degree-Related Field of Study (Required) Experience: Minimum of 4 years-Relevant experience* (Required) Certification(s) and License(s): Skills: Analytical Thinking, C++ Programming Language, Clinical Data Cleaning, Communication, Group Collaboration, Machine Learning Methods, Python (Programming Language), Statistical Methods, Structured Query Language (SQL) OUR PURPOSE & VALUES: Everything we do is about caring for our patients, our members, our students, our Geisinger family and our communities. KINDNESS: We strive to treat everyone as we would hope to be treated ourselves. EXCELLENCE: We treasure colleagues who humbly strive for excellence. LEARNING: We share our knowledge with the best and brightest to better prepare the caregivers for tomorrow. INNOVATION: We constantly seek new and better ways to care for our patients, our members, our community, and the nation. SAFETY: We provide a safe environment for our patients and members and the Geisinger family. We offer healthcare benefits for full time and part time positions from day one, including vision, dental and domestic partners. Perhaps just as important, we encourage an atmosphere of collaboration, cooperation and collegiality. We know that a diverse workforce with unique experiences and backgrounds makes our team stronger. Our patients, members and community come from a wide variety of backgrounds, and it takes a diverse workforce to make better health easier for all. We are proud to be an affirmative action, equal opportunity employer and all qualified applicants will receive consideration for employment regardless to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or status as a protected veteran. Geisinger is committed to making better health easier for the more than 1 million people it serves. Founded more than 100 years ago by Abigail Geisinger, the system now includes nine hospital campuses, a 550,000-member health plan, two research centers and the Geisinger Commonwealth School of Medicine. With nearly 24,000 employees and more than 1,600 employed physicians, Geisinger boosts its hometown economies in Pennsylvania by billions of dollars annually. We offer healthcare benefits for full time and part time positions from day one, including vision, dental and benefits for domestic partners. Perhaps just as important, we encourage an atmosphere of collaboration, cooperation and collegiality. For more information, visit www.geisinger.org, or connect with us on Facebook, Instagram, LinkedIn and Twitter. * Does not qualify for J-1 waiver. We are an Affirmative Action, Equal Opportunity Employer. Women and Minorities are Encouraged to Apply. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of disability or their protected veteran status.

Responsibilities

Lead the full lifecycle of data science projects to develop AI models that predict acute healthcare utilization and improve population health outcomes. Collaborate with clinical leaders to translate complex data into actionable care pathways and mentor junior data scientists.