Senior Healthcare Data Analyst

Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 85 health plans, including many of the top 20, and representing more than 270 million lives, Machinify brings together a fully configurable and content-rich, AI-powered platform along with best-in-class expertise. We’re constantly reimagining what’s possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs.
About the Role
Machinify is rebuilding how health plans audit and pay claims — at scale, with AI. As a Senior Healthcare Data Analyst you will sit at the intersection of domain expertise and data engineering: translating raw claims feeds into trusted canonical models, driving payment integrity insights, and serving as the internal expert on how claims flow through adjudication. This role is for someone who knows healthcare data cold and is excited to pair that knowledge with modern AI tooling to move faster and build smarter.
What You'll Do
Claims mapping & transformation — Design and implement data transformations that convert raw medical and institutional claims (837 EDI, UB-04, CMS-1500) into Machinify's canonical models, applying healthcare coding standards (ICD-10-CM/PCS, CPT, HCPCS) throughout.
Data quality & pipeline validation — Perform detailed audits to detect, troubleshoot, and resolve issues impacting accuracy and completeness; write SQL and Spark SQL queries to validate pipeline output end-to-end.
Platform expertise — Develop deep working knowledge of Machinify's platform—from data ingestion through claims adjudication—so that analytical solutions are accurate, traceable, and business-aligned.
Payment integrity — Identify overpayment patterns, COB issues, and billing edit opportunities by applying expertise in payer policy, CMS regulations, and provider reimbursement methodologies.
AI-assisted workflows — Actively drive AI adoption: prompt and iterate with LLMs to accelerate analysis, identify manual processes ripe for automation, and contribute to AI-assisted pipeline development.
Client onboarding — Support onboarding of new client data feeds, ensuring smooth integration with minimal disruption and clear documentation of transformation logic and lineage.
Cross-functional partnership — Partner with data engineers, data science, clinical staff, and product managers to translate complex healthcare requirements into scalable data solutions; collaborate with data scientists to enable advanced analytics and ML use cases.