Data Analyst, Value-Based Care Analytics

Counterpart Health

1d•$96,000 - $125,000•Remote

About The Position

Counterpart Health is an AI‑powered physician enablement platform that delivers clinical insights to providers at the point of care. Our flagship product, Counterpart Assistant, is embedded into clinicians’ workflows and integrates with EHR systems, helping care teams drive value-based outcomes, close care gaps, and proactively manage chronic disease. As we expand across Medicare Advantage and other value-based care programs, we need analytical talent that can translate complex clinical and financial data into narratives that drive action, for both our internal teams and our payer and provider customers. You will be part of the analytical engine behind Counterpart Health’s value-based care performance insights. This role sits at the intersection of risk adjustment, quality measurement, utilization management, and cost analytics, and you will be expected to own the metrics that matter most to our payer and provider partners. Beyond the numbers, you are a storyteller: you turn dense claims and clinical data into clear, compelling narratives that help customers understand performance, identify opportunities, and take action. You will work closely with Customer Success, Clinical, and Product teams to ensure our analytics are accurate, trusted, and decision-ready.

Requirements

4+ years of hands-on healthcare data analytics experience, with direct exposure to payer or provider customers in a value-based care context.
Deep familiarity with VBC metrics: risk adjustment (HCC coding, RAF scores), utilization (IP admissions, ED visits, readmissions), and cost (PMPM, total cost of care, benchmark vs. actual).
Strong SQL skills, able to write complex queries across claims, eligibility, and clinical datasets in cloud-based warehouses (BigQuery, Snowflake, or similar).
Exceptional data storytelling ability: you can translate analytical findings into clear narratives and visuals that resonate with both clinical and non-technical audiences.
Experience building customer-facing reports and dashboards; comfortable presenting findings to external stakeholders (health plans, provider groups, ACOs).

Nice To Haves

Medicare Advantage experience, including familiarity with CMS risk adjustment models (CMS-HCC v24/v28), Star Ratings methodology, and MA quality bonus payment structures.
Familiarity with clinical terminologies: ICD-10, CPT/HCPCS, NDC, SNOMED, LOINC.
Experience using Python or R for data manipulation, cohort analysis, or statistical modeling.
Background working with EHR or clinical data alongside claims (e.g., for care gap closure, chronic condition identification, or attribution logic).
Prior experience in a health plan, risk-bearing provider group, ACO, or health tech company supporting VBC programs.

Responsibilities

Build and maintain core Value-Based Care (VBC) performance metrics across risk, quality, utilization, and cost domains, including HCC capture rates, RAF scores, PMPM trends, and inpatient/ED utilization rates.
Develop customer-facing analytics, reports, and dashboards that surface actionable performance insights for payer and provider partners in a clear, narrative-driven format.
Analyze Medicare Advantage performance data, including risk adjustment, quality bonus payments, and benchmark performance, to identify gaps, trends, and opportunities at the patient, provider, and market level.
Translate complex data findings into executive-ready narratives: written summaries, slide-ready visuals, and structured QBR materials that tell a coherent story about performance and next steps.
Conduct deep-dive utilization and cost analyses, including high-cost claimant reviews, avoidable utilization patterns, and specialty/pharmacy spend trends, to identify levers for improvement.
Partner with Customer Success and Provider Operations teams to prepare and deliver performance reviews (QBRs, monthly reporting packages) that communicate value and surface priority action areas for each customer.
Collaborate with Data Engineering to validate claims and clinical data pipelines, flag anomalies, and ensure metric consistency across customer populations.
Contribute to the development of scalable analytics infrastructure: reusable SQL libraries, metric definitions, and documentation that enable the team to move faster.