Manager, Risk Adjustment Data Science

Job Description Summary
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The Manager, Risk Adjustment Data Science serves as a strategic and technical leader responsible for advancing the organization’s Burden of Illness and risk adjustment capabilities across Medicare Advantage, MSSP, and Commercial ACO populations. This role combines advanced analytics, machine learning, and AI-driven solutions to improve risk capture, coding accuracy, and overall financial performance in value-based contracts. The Manager will lead the design and deployment of scalable data products, predictive models, and AI-enabled workflows that directly impact RAF performance and total cost of care.

This position partners closely with executive leadership, clinical teams, and risk adjustment operations to translate complex data into actionable strategies. The role requires deep expertise in healthcare data, strong technical leadership, and experience building production-grade data pipelines and ML/AI solutions.‎ 
How will you make an impact & Requirements
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Risk Adjustment & BOI Strategy Leadership
Lead analytics strategy for risk adjustment and BOI performance across MA, MSSP, and Commercial ACO populations
Own RAF performance tracking, suspecting, recapture, and coding optimization initiatives
Translate risk adjustment insights into actionable strategies that drive revenue integrity and value-based performance
Partner with clinical and operational leadership to align analytics with prospective and retrospective RA programs
Serve as a subject matter expert in CMS-HCC models, BOI frameworks, and payer-specific risk methodologies

Data Science, AI & Advanced Analytics 
Design, develop, and deploy machine learning models for risk stratification, suspect identification, and RAF optimization
Build and scale AI-driven solutions to support medical coding, chart review, and clinical documentation workflows
Develop evaluation frameworks and monitoring systems to ensure accuracy, performance, and reliability of ML/AI models
Apply statistical modeling and predictive analytics to identify high-impact intervention opportunities
Explore and implement generative AI / NLP use cases for clinical text and coding optimization

Data Engineering & Scalable Architecture
Architect and maintain end-to-end data pipelines and ETL processes supporting risk adjustment analytics and reporting
Develop scalable data models using dbt within Snowflake and/or Databricks environments
Build production-ready datasets integrating claims, EHR, RAF outputs, and attribution data
Partner with data engineering to optimize data infrastructure, governance, and performance

Reporting, Data Products & Visualization
Lead development of enterprise dashboards and data products tracking:RAF performance and trend analysis
Suspecting and recapture opportunity
Coding accuracy and provider performance
BOI progression across workflows (suspect → visit → claim)

Deliver tools that support both executive decision-making and operational workflows
Automate reporting to support scalable and real-time performance monitoring

Leadership & Cross-Functional Impact
Act as a technical lead and mentor for analysts and data scientists
Partner with FP&A on RAF forecasting, revenue modeling, and contract performance
Collaborate with vendors and internal teams on coding, chart review, and AI initiatives
Drive best practices in data science, analytics, and risk adjustment methodology
Influence enterprise data strategy and analytics roadmap

Education and Experience
Bachelor’s degree in Data Science, Statistics, Mathematics, Economics, Healthcare Analytics, or related field required
Master’s degree (MS, MPH, MBA, or related) preferred
8–10+ years of experience in healthcare analytics, with deep focus on risk adjustment and value-based care
Demonstrated experience in:Medicare Advantage risk adjustment (CMS-HCC)
BOI / RAF performance analytics
Machine learning or predictive modeling in healthcare
Building production data pipelines and analytics workflows

Experience working with claims, EHR, and CMS data (MMR, MAO-004, etc.) strongly preferred 

Required Technical Skills
·         Advanced SQL (expert-level)
·         Python (machine learning, data processing, automation)
·         Snowflake + dbt (data modeling and transformation)
·         Databricks or similar distributed compute platforms
·         Tableau (or equivalent BI tools)
·         Experience with ML frameworks (scikit-learn, etc.)
·         Familiarity with AI/NLP applications in healthcare data
·         Strong understanding of risk adjustment data flows (RAPS/EDPS)