Data Scientist V - Medicare, ACA, Risk Adjustment

Remote from any KP location in CA, OR, CO, WA, GA, MD, VA, HI or D.C. Only.

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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**

The Prospective Risk Adjustment Operations team is seeking a Data Scientist to support scoping, deploying, and reporting out on projects to support prospective risk adjustment projects. This pivotal role will support the development of foundational reporting and analytical frameworks crucial for identifying and prioritizing prospective risk initiatives, developing and supporting comprehensive reporting and insightful visualization of opportunities and outcomes, and directly supporting strategic decision-making and operational excellence.

Ideal candidates will possess robust analytical skills and a proven ability to translate complex data into actionable business intelligence within a dynamic healthcare environment. This position offers a significant opportunity to contribute to the organization's continued success in risk adjustment.

This role requires a background in technical coding (i.e SQL, Python, R etc.) or other statistical modeling programs. Familiarity with data science disciplines (i.e machine learning, predictive analytics, data visualization etc.), data modeling is preferred.

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 data acquisition and ingestion of raw data from multiple data sources and data formats by overseeing the transformation, cleansing, and storing of data for consumption by downstream processes; writing and optimizing diverse and complex SQL queries; and demonstrating expertise of database fundamentals.
• Serves as an expert in the analysis and investigation of complex data sets by ensuring optimum data visualization methods are employed; determining how best to manipulate data sources to discover patterns, spot anomalies, test hypotheses, and/or check assumptions; and reviewing and verifying summaries of key dataset characteristics.
• Leads the selection, manipulation, and transformation of data into features used in machine learning algorithms by leveraging and demonstrating expertise in techniques to conduct dimensionality reduction, feature importance, and feature selection.
• Trains statistical models by selecting and leveraging algorithms and data mining techniques; leading model testing by ensuring the proper use of various algorithms to assess the input dataset and related features; and applying…