Risk Adjustment Consulting, Experienced

Your Role

The Enterprise Retrospective Chart Review team oversees end to end retrospective coding operations, ensuring the accuracy, completeness, and compliance of medical record review across all risk adjusted lines of business. The team manages enterprise workflows, vendor performance, coding quality, audit readiness, and cross functional alignment with Clinical, Compliance, and Risk Adjustment partners.

Your Knowledge and Experience

• Requires a college degree or equivalent experience
• Requires 3 years of prior relevant experience within healthcare, preferably Risk Adjustment
• Requires practical knowledge of Risk Adjustment obtained through professional experience and advanced education
• Requires familiarity with AI tools, including prompt engineering and workflow automation
• Requires experience building dashboards or visual reports that support operational decision making
• Requires strong communication skills for concise summaries and effective cross functional collaboration

Hybrid

This role requires employees to be in-office based on our hybrid workplace model, balancing purposeful in-person collaboration with flexibility. For most teams, this means coming into the office two days each week. Employees living more than 50 miles from an office location will work with their manager to determine in-office time based on business need.

Your Work

In this role, you will:

• Analyze under the supervision of the core principles and functionality of decision and descriptive analytics including analyzing historical data, past performance and trends
• Develop and run programs that apply to analytic decision techniques
• Contribute to successful programs for the team, engaged in typically one area related to vendor oversight, internal cross functional partnerships, tracking performance and measuring results
• Continuously learn about plan data including claim, premium, membership, and risk score assignment
• Conduct data analysis, documenting and verifying the assumptions used in computations such as those used in member risk data and score submissions and establishing revenue accruals
• Analyze operational performance to identify trends, risks, and improvement opportunities and to forecast retrieval volumes, coding workloads, and capacity needs using both traditional analytics and AI‑driven modeling
• Design AI‑enabled workflows that enhance chart ingestion, data validation, and operational reporting for risk adjustment, using AI tools to improve efficiency and consistency
• Automate data processes by building Python and SQL solutions and applying AI applications to streamline vendor file processing, recurring reporting, and ad hoc analytics
• Produce operational reporting through dashboards and scorecards that summarize retrieval performance, coding outcomes, and risk adjustment trends, supported by AI‑assisted insight generation
• Manage data quality by ingesting weekly vendor data loads, performing QA checks, validating accuracy, and applying AI tools to support anomaly detection and data review

Your Work

In this role, you will:

• Analyze under the supervision of the core principles and functionality of decision and descriptive analytics including analyzing historical data, past performance and trends
• Develop and run programs that apply to analytic decision techniques
• Contribute to successful programs for the team, engaged in typically one area related to vendor oversight, internal cross functional partnerships, tracking performance and measuring results
• Continuously learn about plan data including claim, premium, membership, and risk score assignment
• Conduct data analysis, documenting and verifying the assumptions used in computations such as those used in member risk data and score submissions and establishing revenue accruals
• Analyze operational performance to identify trends, risks, and improvement opportunities and to forecast retrieval volumes, coding workloads, and capacity needs using both traditional analytics and AI‑driven modeling
• Design AI‑enabled workflows that enhance chart ingestion, data validation, and operational reporting for risk adjustment, using AI tools to improve efficiency and consistency
• Automate data processes by building Python and SQL solutions and applying AI applications to streamline vendor file processing, recurring reporting, and ad hoc analytics
• Produce operational reporting through dashboards and scorecards that summarize retrieval performance, coding outcomes, and risk adjustment trends, supported by AI‑assisted insight generation
• Manage data quality by ingesting weekly vendor data loads, performing QA checks, validating accuracy, and applying AI tools to support anomaly detection and data review