Regulated Healthcare Data & MLOps Platform

Mon, 01 Jun 2026 00:00:00 +0000

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.

Healthcare Data Partnerships to AI-Ready Assets

Fri, 15 May 2026 00:00:00 +0000

Healthcare AI programs depend on much more than model development. The hard part is often deciding which labeled and unlabeled data is worth pursuing, how to structure collaboration, and how to make the resulting assets reusable, governed, and fit for training and evaluation.

My work in this area includes:

Prioritizing data acquisition, annotation, and platform investments by clinical-domain fit, downstream model value, partner readiness, technical feasibility, fit-for-purpose criteria, regulatory constraints, cost, and reuse potential.
Driving data access paths, data-use agreements/SOWs, contribution standards, licensing decisions, vendor/tooling selection, active-learning and annotation operations, pricing and staged-pilot paths, and release/reuse planning.
Aligning data contributors, clinical experts, data stewards, platform engineers, AI/algorithm teams, regulatory stakeholders, and business leaders around durable data products.
Establishing public-safe operating patterns for de-identification, provenance, access, quality, data lifecycle ownership, and governance.

The goal is to turn collaborations into data assets that can feed multiple programs over time: product AI, algorithm validation, real-world evidence, post-market surveillance, clinical informatics, and future multimodal/physical-AI workflows.

Healthcare Data Strategy | Jason Thomas, PhD

Regulated Healthcare Data & MLOps Platform

Healthcare Data Partnerships to AI-Ready Assets