Data Scientist V - Risk Adjustment: Medicare, Medicaid, ACA

** PLEASE NOTE: Salary ranges are geographically based and the posted range reflects the Northen CA region. Lower salary ranges will apply for other labor markets outside of NCAL Overview: The Risk Adjustment Strategic Analytics team is seeking a Data Scientist V to play a strategic and technical role in advancing Government Programs- Risk Adjustment through the application of advanced data science techniques, including machine learning, predictive modeling, and statistical inference. This senior position is responsible for designing and implementing scalable, end-to-end data science solutions that enhance risk score accuracy, support regulatory compliance, and drive financial performance across Medicare Advantage, ACA, and Medicaid programs. Using Python and cloud-based analytics platforms, the Data Scientist develops and manages machine learning models to forecast risk, identify outreach opportunities, and evaluate program effectiveness. The role requires deep expertise in healthcare data (e.g., claims, encounter, enrollment), strong knowledge of HCC risk adjustment methodologies, and the ability to translate complex analytical outputs into actionable insights for both technical and executive audiences. As a thought leader, the Data Scientist collaborates across clinical, actuarial, and operational teams, mentors junior data scientists, and fosters a data-driven culture to support innovation and strategic decision-making across the organization.

This senior individual contributor is primarily responsible for leading the design and development of data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats. This role is also responsible for leading the development of detailed problem statements outlining hypotheses and their effect on target clients/customers, serving as an expert in the analysis and investigation of complex data sets, leading the selection, manipulation and transformation of data into features used in machine learning algorithms, training statistical models, leading the deployment and maintenance of reliable and efficient models through production, verifying and ensuring model performance, and partnering with internal and external stakeholders across domains to develop and deliver statistical driven outcomes.

Promotes learning in others by communicating information and providing advice to drive projects forward; builds relationships with cross-functional stakeholders. Listens, responds to, seeks, and addresses performance feedback; provides actionable feedback to others, including upward feedback to leadership and mentors junior team members. Practices self-leadership; creates and executes plans to capitalize on strengths and improve opportunity areas; influences team members within assigned team or unit. Adapts to competing demands and new responsibilities; adapts to and learns from change, challenges, and feedback. Models team collaboration within and across teams. Conducts or oversees business-specific projects by applying deep expertise in subject area; promotes adherence to all procedures and policies. Partners internally and externally to make effective business decisions; determines and carries out processes and methodologies; solves complex problems; escalates high-priority issues or risks, as appropriate; monitors progress and results. Develops work plans to meet business priorities and deadlines; coordinates and delegates resources to accomplish organizational goals. Recognizes and capitalizes on improvement opportunities; evaluates recommendations made; influences the completion of project tasks by others. Leads the development of detailed problem statements outlining hypotheses and their effect on target clients/customers by ensuring comprehensive and accurate definitions of scope, objectives, outcome statements and metrics. Leads the design and development of data pipelines and automation for