Senior AI PM for Data and Governance

Senior AI Product Manager, Data, Analytics, Evaluation & Governance

Transform healthcare with us.

At Qualified Health, we're redefining what's possible with Generative AI in healthcare. Our infrastructure provides the guardrails for safe AI governance, healthcare-specific agent creation, and real-time algorithm monitoring, working alongside leading health systems to drive real change.

This is more than just a job. It's an opportunity to build the future of AI in healthcare, solve complex challenges, and make a lasting impact on patient care. If you're ambitious, innovative, and ready to move fast, we'd love to have you on board.

Qualified Health is seeking a Senior AI Product Manager to own the connected core of our platform: data, analytics, evaluation, and governance. That means the data layer as a product, the analytics and insights surfaces our customers see, the evaluation frameworks that tell us whether AI outputs meet the bar, the governance spine that makes all of it safe to run in healthcare, and the contracts that connect the data organization to every product we ship.

These four are one system: data feeds the products, analytics measures them, evaluation proves they work, and governance makes them trustworthy at scale.

This is an integrator and enabler role, not a control role. The data platform and analytics teams own their domains, their delivery, and their technical decisions; that does not change. What is missing today is the connective tissue: product-shaped data work is spread across a data platform team, an analytics and new-product team, and a platform engineering organization, and nobody owns the seams between them.

You are that person. You make these teams faster by absorbing the coordination work that currently lands on their leads: writing the acceptance criteria before build, defining the contracts between teams, running intake and prioritization for analytics asks, and making sure what gets built once is reusable everywhere.

When a care gap product needs a data pipeline, you make sure it is scoped once, built to generalize, and reusable for the next customer. When a dashboard metric ships, you make sure it is defined once, governed, and consistent everywhere it appears. When engineering and data disagree about who owns a layer, you are the person who has already written it down.

Your success is measured by whether the data platform and analytics teams say you make them faster. If they route around you, the role has failed.

You will sit on the Platform pod, reporting to the SVP of Product, and partner daily with the data platform lead, the analytics and new product development lead, engineering leadership, and peer product managers.

You own products, contracts, and processes. The teams own their domains.

• The data layer as a product: The serving contracts, gold-layer marts, and semantic views that assistants, chat, workflows, and dashboards consume. Defined once, versioned, and stable enough that the data platform can refactor underneath without breaking products. The data platform team builds and owns the platform; you own the product definition of what it serves and to whom.

• Analytics and insights products: Customer-facing dashboards, usage and adoption analytics, cost and token observability, and the KPI catalog. One definition per metric, a lightweight vetting process for anything customer-facing, and no metric proliferation. The analytics team owns the builds; you own intake, prioritization, and the catalog so requests stop arriving from every direction at once.

• Data product pipelines for clinical products: The data and scoring pipelines behind products like Care Gap Optimizers: acceptance criteria written before build, generalization requirements set at the start, and clear seams between data, AI engineering, and application engineering.

• Evaluation as a product: The frameworks, datasets, and gates that determine whether an AI output is good enough to ship and stays good enough in production: validation criteria before build, generalization requirements at full population scale, and post-go-live monitoring. Evaluation stops being a per-team…