Location: REMOTE / Nashville, Tennessee
This job allows you to work remotely.
Our client is transforming the experience of specialty care. Their comprehensive care program takes a profoundly personal, evidence-based approach to improving patient outcomes for joint, back, and muscle conditions. By carefully assessing patients’ symptoms, health histories, preferences, and goals with predictive data and the latest evidence-based guidelines, they help patients choose and navigate the most effective treatment pathway every step of the way.
The company values the experiences and perspectives of individuals from all backgrounds. They are a highly collaborative, curious, and determined team passionate about scaling a high-growth start-up to improve the lives of those in pain. The company is remote-first with a corporate office located in Nashville.
This Role:
Our client is a value-based musculoskeletal (MSK) care company that uses clinical expertise and data to help patients with joint, back, and muscle conditions get the right level of care at the right time. They are evolving their operating model to dynamically match each patient's care plan to their likelihood of improvement and the expected return on that investment.
To do this effectively, they need a modern data and AI platform that can move quickly, scale reliably, and meet the rigorous compliance standards healthcare requires. You will own that build — setting the data strategy, designing the architecture, guiding vendor decisions, and partnering closely with engineering, product, clinical, and operations teams to bring it to life. This is a role that sits at the intersection of strategy and hands-on architecture; you'll be equally comfortable discussing direction with the CTO and getting into the details of a data integration or model evaluation.
You Will:
•Own the multi-year data strategy supporting operating model optimization and expansion beyond MSK; frame tradeoffs for the executive team and protect strategic data moats including canonical patient and provider graphs, proprietary outcome labels, and yield-calibrated decisioning IP
•Architect the platform end-to-end: ingestion, event bus, identity resolution, lakehouse, feature store, ROI Gate decisioning, action orchestration, and observability; define the event taxonomy, schema registry, and data contracts that govern how every source flows in
•Evaluate and select vendors across clearinghouses, clinical networks, claims systems, provider intelligence, conversational AI, and CDPs; negotiate export rights, schema stability, and BAA scope; build payer-neutral patterns from day one
•Set and enforce standards for HIPAA, PHI, and AI governance: classification at ingestion, field-level access controls, infrastructure-level scrubbing, vendor data governance, training-data lineage, and consent chains
•Translate architecture into staffed delivery across engineering teams; collaborate with clinical leadership on patient reported outcomes, extraction accuracy, and outcome labels; partner with operations to make intake and data aggregation meaningful
Must Have Skills:
•10+ years working with healthcare data, including 4+ years in architecture or strategy leadership
•Deep hands-on knowledge of healthcare data standards: X12 EDI (270/271/276/277/278/834/835/837), FHIR R4, HL7 v2 (especially ADT), CCD/C-CDA, NCPDP, and experience integrating with payers, EHRs, clearinghouses, and HIEs
•Experience partnering with AI teams on predictive model development; able to serve as the liaison between AI modeling and data platform
•Experience designing production data platforms at scale—streaming and batch—with managed Kafka or equivalent, lakehouse architectures (Snowflake, Databricks, BigQuery), dbt-style orchestration, and modern observability
•Solid grounding in ML/AI systems: feature stores, point-in-time correctness, model lifecycle, and NLP for clinical text
•Direct experience with patient identity resolution (deterministic and probabilistic) and tokenization (Datavant or equivalent)
•Working knowledge of value-based care economics: MLR, attribution, episode costing, risk adjustment, and how reimbursement models shape data requirements
•Demonstrated executive presence: framing tradeoffs, defending recommendations, and staying technically credible
•HIPAA-fluent; engineers PHI minimization, BAA structures, and audit requirements as first-class concerns
