Medical Data Analyst (Actuarial Analyst) job opportunity at Reveal HealthTech.

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Date2026-01-14T17:21:46.073Z bot
Reveal HealthTech Medical Data Analyst (Actuarial Analyst)
Experience: 3-years
Pattern: Full-time
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loacation Bengaluru, India
loacation Bengaluru....India

Thank you for considering the Medical Data Analyst position at Reveal Health Tech. We are a Series A–stage IT startup based in the US and India, delivering technology-driven solutions for the healthcare and life sciences industry as we scale toward our next phase of growth. About the Role We’re looking for a Medical Data Analyst who can turn messy healthcare data into crisp, decision-ready insights. You’ll build end-to-end analytics solutions, support value-based care initiatives, and partner closely with provider groups, specialty groups, and payers to surface opportunities that improve outcomes, margin performance, and total cost of care. You’ll also play a key role in analyzing actuarial and contract performance dynamics- evaluating utilization trends, PMPM/TCOC, and contract benchmarks to ensure programs are financially aligned and sustainable. What You’ll Do Build analytics products: Own the full Power BI lifecycle—data modeling, DAX, visuals, RLS, deployment, and documentation. Enable value-based care (VBC): Define and track measures (e.g., readmissions, avoidable ER/IP, adherence, risk acuity, PMPM/TCOC), attribution logic, and cohorting. Actuarial & contract performance analysis: Support actuarial-oriented analyses including PMPM + risk-adjusted cost modeling, benchmarking, savings reconciliation, trend analysis, and scenario modeling. Data wrangling & QA: Ingest, clean, validate, and reconcile claims, eligibility, EMR, and care-management data. Performance tracking: Build scorecards and drill-downs for executives, clinicians, care teams, actuarial partners, and payer counterparts. Operationalize insights: Create recurring datasets, alerts, and self-service dashboards; document metric definitions, lineage, and reconciliation rules. Governance & compliance: Uphold HIPAA/privacy requirements and contribute to data quality standards. Ad-hoc analysis: Run cohort analyses, payer/provider comparisons, utilization analysis, risk-adjusted benchmarking, leakage patterns, and trend studies. Minimum Qualifications 3+ years in healthcare analytics, actuarial services, population health, or payer/provider performance. Strong Power BI (DAX, Power Query/M, data modeling, RLS). Solid SQL (CTEs, window functions, performance tuning) and comfort with large datasets. Hands-on with claims/eligibility (837/835 concepts, CPT/HCPCS/ICD-10, NPI, taxonomy), EMR extracts, and payer/provider data nuances. Experience collaborating with provider groups and payers—requirements gathering, walkthroughs, and iteration. Understanding of value-based care concepts (TCOC, PMPM, savings reconciliation, attribution, risk adjustment, quality measures). Ability to convert ambiguous analytic questions into concrete business metrics with documented methodologies. Preferred Qualifications Experience with actuarial or contract modeling, shared savings, capitation, bundles, or delegated risk arrangements. Experience in cardiology/specialty care, care-management, or remote patient monitoring. Python or R for data prep/statistics; exposure to dbt or ETL tooling. Cloud data platforms (Azure Synapse/SQL, Databricks, Snowflake, or Redshift). Familiarity with HEDIS/STARs, RAF/HCC models, quality specifications, and performance reporting. Data governance: lineage, cataloging, metric definitions, reconciliation rules. Tech Stack You May Use Here BI: Power BI (Service, Desktop, Deployment Pipelines) Data: SQL Server/Azure SQL/Synapse (or similar), Power Query/M, APIs/flat files Scripting (nice-to-have): Python (pandas), dbt DevOps: Git, CI/CD for BI artifacts, Workspaces, RLS/ALS

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