Principal Privacy Product Manager, Health AI

Microsoft•Redmond, WA

7d•$142,800 - $304,200

About The Position

Microsoft AI is building the next generation of AI products for health, where privacy decisions carry profound implications for trust, safety, and product quality. Operating at the intersection of healthcare innovation and privacy means handling sensitive health and personal data at scale while navigating a complex, multi-jurisdictional legal and policy landscape. As a Principle Privacy Product Manager for our Health vertical within Microsoft AI, you will be the privacy architect for our health AI products like Copilot Health and frontier AI model development. You will work across engineering, clinical, and product teams to drive privacy strategy, define product controls, and lead implementation execution specific to health contexts. You will bring deep health data acumen to inform how we design, build, and operate AI systems that handle sensitive information. You will combine strong privacy and policy expertise with engineering muscle to ensure our health AI products are both innovative and trustworthy. We are searching for a Privacy Product Manager with deep health data and privacy expertise to lead execution of our health privacy strategy and partner with engineering to define scalable product controls across our portfolio. The right person combines user-rights judgment, health domain fluency, and technical depth to enable frontier health AI responsibly.

Requirements

Bachelor's Degree AND 8+ years experience in product/service/program management or software development OR equivalent experience
6+ years experience disrupting a market for a product, feature, or experience (e.g., competitive disruption, taking the place of an established competing product)
4+ years of practical experience creating and maintaining compliance programs and conducting privacy reviews.

Nice To Haves

Bachelor's degree in Computer Science or a closely related discipline
Experience translating policy and privacy requirements into shipped product controls in partnership with engineering.
Experience leading privacy/compliance engineering programs for AI/ML products or other data-intensive platforms at scale.
Hands-on familiarity with regulations related to health verticals, such as HIPAA, GDPR, and similar.
Practical experience in health privacy, health policy, and/or health data governance.
Working knowledge of health regulations and industry frameworks, including data sensitivity, governance expectations, and common privacy risk patterns.
Hands-on experience with privacy engineering practices such as data flow mapping, data classification, consent infrastructure, threat modeling, DSR tooling, retention enforcement, and telemetry governance.
Proven ability to lead ambiguous cross-functional work and influence senior leaders without authority, while communicating clearly to engineering and executive audiences.
IAPP Certified Information Privacy Professional (CIPP) or Certified Information Privacy Manager (CIPM).
Demonstrated team and project management excellence on medium-to-large scale projects, including managing scope, schedule, and budget.

Responsibilities

Be the privacy partner to health engineering, product, and clinical stakeholders, guiding privacy decisions across the end-to-end product experience, including feature design, user flows, defaults, and sensitive data use.
Set and prioritize privacy controls for health AI scenarios, translate them into clear engineering requirements, and drive implementation and adoption across the lifecycle.
Lead privacy design reviews for health AI features, including data handling, model behavior, and user-facing transparency and choice experiences.
Own data governance expectations across the health product lifecycle, including collection, use, sharing, retention, and deletion.
Monitor emerging health and data policy developments, assess product impact, and drive prioritized control or experience changes with product and engineering partners.
Drive privacy program improvements through repeatable patterns, tooling recommendations, governance and effectiveness metrics, and engineering enablement.