• Design and develop enterprise-wide data governance programs • Define governance operating models and accountability mechanisms • Develop policies and processes for data management • Partner with business and data product teams

• 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

• Apply statistical analysis and experimentation to evaluate generative AI, agentic AI, and other AI-enabled applications • Develop and maintain Responsible AI measures, benchmark datasets, test suites, and evaluation criteria • Evaluate AI systems for fairness, bias, explainability, transparency, hallucination, safety, reliability, and robustness • Use evaluation results to support the design, testing, selection, and ongoing assessment of AI-enabled business applications • Establish performance baselines and help identify changes in model or application behavior over time • Design and execute proofs of concept for new Responsible AI evaluation approaches • Assess emerging Responsible AI tools, frameworks, and methodologies for validity, limitations, and business applicability • Support the integration of Responsible AI testing into application development, quality assurance, and governance processes • Partner with engineering, architecture, quality assurance, governance, and business teams to align evaluation methods with use case requirements • Create and maintain model cards, evaluation reports, methodology documentation, and related guidance • Communicate findings, limitations, risks, and recommendations to technical and nontechnical stakeholders • Contribute to Responsible AI training, internal education, applied research, and industry engagement • Monitor emerging technologies and evaluation methods related to generative AI, agentic AI, and Responsible AI.

• Lead the development of models and decision systems that improve how Honor matches caregivers and clients, constructs sustainable schedules, allocates capacity, prioritizes operational work, and responds to changing conditions. • Your team will work directly with Product, Engineering, and Care Operations to turn difficult operating problems into production systems that improve measurable outcomes. • Contribute to Honor’s broader Data Science and Applied AI direction. • Help shape the team’s roadmap and remain hands-on as a player-coach. • Partner with Product, Engineering, and Operations from problem definition through deployment, adoption, and iteration. • Establish strong standards for evaluating, monitoring, and responsibly deploying machine learning and AI-enabled systems. • Lead, coach, and grow the team while remaining hands-on in high-priority projects as a player-coach.