Lead Analyst, National Risk Adjustment Predictive Analytics

Accountabilities:

• • Lead analytical support for prospective and retrospective risk adjustment intervention strategies, including tracking progress, outcomes, and financial or operational impact.
  • Design and develop systems to track risk scores, intervention outcomes, market performance, and results across multiple lines of business.
  • Develop automated and ad-hoc quality assurance reporting modules supporting Medicaid, Marketplace, Medicare, and Medicare-Medicaid programs.
  • Support the development of automated suspect, targeting, and ranking engines to identify opportunities for risk adjustment interventions.
  • Analyze complex healthcare datasets, including claims, pharmacy, laboratory, utilization, financial, and performance data, using data mining, validation, scrubbing, and root-cause analysis techniques.
  • Identify anomalies, outliers, changing trends, and improvement opportunities using statistical methodologies, and translate findings into concise executive-level recommendations.
  • Apply extrapolation, interpolation, forecasting, and other analytical techniques to predict trends in cost, utilization, risk scores, and performance.
  • Partner with cross-functional stakeholders and leaders to define business requirements, clarify data needs, communicate findings, and escalate issues when appropriate.
  • Conduct preliminary and post-implementation impact analyses for changes to analytics logic, source code, reporting modules, and data warehouse platforms.
  • Manage changes and upgrades to data warehouse and analytics environments while maintaining continuity and transparency for end users.
  • Support special projects requested by internal teams, regulatory bodies, contracting organizations, and other external stakeholders.
  • Monitor applicable CMS and state risk adjustment regulations, incorporate regulatory changes into analytics activities, and provide training and education as needed.
  • Develop training materials and deliver guidance to help analysts understand analytical processes, solutions, and reporting designs.
  • Train new and existing team members while promoting a collaborative, agile, and continuous-improvement culture.

  Requirements

  • Minimum of 4 years of experience developing complex SQL queries, functions, procedures, and data designs within relational database environments.
  • Hands-on experience with Microsoft T-SQL, Databricks SQL, Power BI, SQL Server Integration Services (SSIS), and SQL Server Reporting Services (SSRS).
  • Experience applying predictive modeling, statistical analysis, forecasting, and performance tracking techniques within healthcare, quality, HEDIS, risk adjustment, finance, or health plan environments.
  • Demonstrated ability to work with complex datasets and quantify financial, utilization, operational, and performance metrics.
  • Experience with cloud or distributed data platforms such as Microsoft Azure, Amazon Web Services (AWS), or Hadoop.
  • Strong analytical thinking, attention to detail, problem-solving ability, and experience performing structured root-cause analysis.
  • Strong business acumen with the ability to connect analytical findings to broader strategic and operational objectives.
  • Excellent communication skills, including the ability to explain complex technical and analytical concepts to non-technical stakeholders and executives.
  • Proven ability to lead cross-functional initiatives and deliver measurable results within highly matrixed organizations.
  • Ability to manage multiple priorities and meet demanding deadlines in a fast-paced, agile environment.
  • Self-directed approach, strong ownership, adaptability, and a continuous-improvement mindset.
  • Strong written and verbal communication skills and proficiency with Microsoft Office applications.
  • Familiarity with Python and R programming or broader data science techniques is preferred.

  Benefits

  • Competitive annual salary range of $72,370.82–$156,803.45, with actual compensation dependent on factors such as geographic location, experience, education, and skill level.
  • Comprehensive benefits and compensation package.
  • Full-time employment with a U.S.-based opportunity.
  • Opportunity to work on enterprise-scale healthcare analytics spanning multiple lines of business.
  • Exposure to advanced predictive analytics, risk adjustment, forecasting, data engineering, and business intelligence initiatives.
  • Collaboration with cross-functional healthcare, analytics, finance, quality, and business teams.
  • Professional development through training, knowledge sharing, and opportunities to contribute to strategic analytical projects.