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 bachelor's degree in health management, mathematics, statistics, computer science, or a related field

Requires 3+ years of experience building reproducible analytic pipelines (end-to-end data workflows from raw extraction to structured reporting) and technical documentation using SQL, SAS, or Python on health plan data (claims, premium, and membership).

Requires experience building dashboards or visual reports that support operational decision making

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