Regulated Healthcare Data & MLOps Platform

I lead healthcare data operations and AI/MLOps platform strategy for medical-device R&D, translating product roadmaps, clinical needs, engineering needs, regulatory constraints, and business priorities into reusable data products and platform capabilities.

Public-safe scope includes:

• Building a distributed platform function across data sourcing, curation, annotation, data/ML engineering, infrastructure, MLOps, stewardship, governance, and clinical informatics.
• Translating product roadmaps into data demand/supply models, labeled and unlabeled data requirements, acquisition and annotation priorities, fit-for-purpose criteria, platform roadmaps, investment logic, success metrics, and executive dashboards.
• Supporting 100+ developers, analysts, and product AI/algorithm teams through governed data access, de-identification/privacy controls, observability, audit logging, and reusable data-serving patterns.
• Architecting lakehouse and MLOps foundations for DICOM-linked clinical/imaging data, real-world evidence, and multimodal medical data.
• Establishing practices for lineage, reproducibility, dataset/model versioning, data contracts, train/validation/test governance, quality gates, and blinded held-out evaluation sets.
• Making build-vs-integrate decisions across internal platforms, vendor tools, licensed data, cataloging, warehouse/BI, and lakehouse architecture.

This work reflects the operating model I am most interested in scaling: data strategy, partnerships, platform engineering, governance, and validation coming together so healthcare AI programs can become durable assets rather than one-off projects.