Product Manager, Health AI

bostonscientific•Maple Grove, MA

15d•$106,800 - $202,900•Hybrid

About The Position

At Boston Scientific, you’ll have the opportunity to harness all that’s within you by working in teams of diverse and high-performing employees, tackling some of the most important health industry challenges. With access to the latest tools, information, and training, we’ll help you advance your skills and career—supported in progressing, whatever your ambitions. The healthcare industry is experiencing rapid digital transformation, reshaping patient experiences, clinical workflows, and expectations. Boston Scientific is advancing science for life by delivering secure, scalable, and responsible AI solutions that improve outcomes and enable new models of care. As the Product Manager, Health AI , you will drive strategy execution and delivery for a portfolio of Health AI initiatives. You will translate clinical and business needs into well-governed programs, align stakeholders across divisions, and manage external partners and vendors to deliver measurable value. In this role, you will work closely with AI Engineering, Platform teams, Enterprise Architecture, Data and AI Governance stakeholders, and Business Units to ensure solutions are feasible, integrated with enterprise standards, validated for clinical and technical performance, and ready for scaled adoption.

Requirements

Bachelor’s degree in business, engineering, computer science, biomedical engineering, or a related field.
Minimum of 7 years' experience in program management, product management, or digital delivery within a large, matrixed organization.
Demonstrated experience delivering AI and machine learning-enabled products or platforms, including vendor-led solutions, from discovery through launch and scale.
Hands-on experience leading cross-functional delivery across Clinical, IT or Enterprise Architecture, Security and Privacy, Regulatory and Quality, and external partners.
Working knowledge of digital and AI solutions, including clinical decision support, healthcare data interoperability, imaging AI, and diagnostic and predictive algorithms.
Strong vendor management and partnership evaluation skills, including technical due diligence, value hypothesis definition, and validation planning.
Strong analytical, problem-solving, and communication skills, with the ability to influence without direct authority and navigate ambiguity.

Nice To Haves

Experience with imaging AI and computer vision products and associated clinical validation methods.
Experience with AI and machine learning algorithms used in clinical decision support and physician workflow management.
Experience with AI and machine learning-enabled patient diagnostics and longitudinal care, including patient identification and stratification.
Experience with Responsible AI practices, model risk management, and post-market monitoring for AI solutions.
Familiarity with U.S. and EU regulatory considerations for AI-enabled medical software, including SaMD and emerging AI regulations.
Certification in Agile and or Product Management, such as CSPO, PSPO, SAFe, PMP, or equivalent.
Experience scaling enterprise vendor partnerships and onboarding solutions for multi-division reuse.

Responsibilities

Deliver cross-divisional Health AI initiatives with clinical, R&D, and operational stakeholders by translating needs into well-defined programs, platform-enabled capabilities, and release plans with clear milestones, dependencies, and success criteria.
Facilitate end-to-end roadmaps and execution by establishing phase gates from discovery through pilot, validation, launch, and scale, ensuring readiness across clinical, technical, financial, and operational dimensions.
Establish and manage enterprise Health AI preferred partnerships and vendor ecosystems that divisions can leverage to accelerate solution development and deployment, driving cost sharing, standardized onboarding, enterprise agreements, and reusable integrations.
Partner with divisional product teams to define and mature strategic programs, including imaging algorithm development, data interoperability, and hospital solution implementation, ensuring each initiative has clear roadmaps, financial plans, governance, and resourced execution teams.
Support divisions in defining integration and implementation strategies for third-party data sources, such as EMR and EHR systems, aligning to enterprise standards for data governance, privacy, cybersecurity, and interoperability, including HL7 and FHIR where applicable.
Enable divisions to build AI and machine learning-enabled diagnostics and longitudinal care solutions by coordinating cross-functional requirements, risk assessments, and controls to support safe AI-enabled workflows across care settings.
Partner with divisions to accelerate physician workflow efficiency and improve clinical decision support by supporting adoption planning, training approaches, and change management with clinical and hospital stakeholders.
Coordinate with AI Engineering, clinical experts, and divisional teams to set imaging AI initiatives up for success, including validation plans, dataset strategy, performance metrics, bias and risk considerations, and post-deployment monitoring expectations.
Support hospital-ready scalability by aligning deployment models, cybersecurity requirements, support models, and operational SLAs, while promoting reusable, enterprise-grade capabilities that reduce duplication and accelerate time to value.
Lead partnership selection and validation for Health AI vendors, including strategic fit, technical and Responsible AI due diligence, and clinical value assessment.
Coordinate vendor onboarding to meet enterprise needs across divisions, establishing long-term collaboration models, integration standards, and governance expectations.
Manage vendor deliverables, contractual milestones, and performance in partnership with divisions, Procurement, Legal, Privacy, and Security teams.
Champion agile and compliant Health AI delivery practices in collaboration with divisions and data and AI delivery teams, leveraging AI-enabled tools for backlog management, sprint planning, and progress reporting.
Collaborate with Responsible AI, Regulatory, and Quality teams to standardize documentation, decision gates, and risk management artifacts, including alignment with software lifecycle controls and SaMD expectations where applicable.
Collaborate with divisions and delivery teams to define and report KPIs for adoption, outcomes, operational impact, and financial value realization, supporting corrective actions when needed.
Provide regular updates to leadership and governance councils, clearly communicating progress, risks, decisions required, and recommended trade-offs.