Seen Health

Data Scientist

• AI-native data engineering: architect the whole data stack — ingestion, orchestration, warehouse, transformation, and analytics — and direct a fleet of AI agents to build and maintain it. You set the architecture, engineering practices, and quality bar; agents do almost 100% of coding
• Own the pipelines that reliably move clinical (FHIR/EHR), claims (EDI 837/834/820), pharmacy, financial, operational, and internal data through Dagster, dbt, and BigQuery
• Build new pathways for data in and out, structured and unstructured: state and payer feeds, vendor SFTP drops, partner APIs, regulatory exports, CMS audit deliverables, plus call recordings, visit notes, faxes, and scanned records that live outside any system of record today
• Build and evolve a metadata layer that guides humans and agents: a catalog of what data exists and what it means, and a semantic layer on top so every dashboard, analyst, and agent is querying the same definition of “member,” “risk score,” and “cost of care”
• Turn data into decisions: ship the metrics, dashboards, and insights that clinicians, operators, finance, and regulators rely on; measure success in decisions made faster and outcomes improved
• Own the analytics behind risk adjustment and clinical quality: HCC/RAF capture, care gap closure, quality measure reporting, and the burden-of-illness view that connects clinical documentation to financial performance
• Build the intelligence layer for the company: capture what happens on every call, visit, and meeting through agents and recordings, and turn it into structured signal instead of knowledge that lives in someone’s head — then use it to give leadership proactive, org-level visibility into what’s breaking before anyone has to ask
• Lead the data team: hire and manage a mix of internal and external resources, set data roadmap, quality standards, and PHI/security posture

Head of Data