Senior Product Manager - AI Trust & Transparency
athenahealth
- Location
- Boston, Massachusetts, United States of America
- Employment
- Full Time
- Work model
- On-Site
- Level
- Senior
- Posted
- Sep 10, 2026
Skills
About this role
Join us as we work to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all.
Position
Summary Help shape how trustworthy AI is designed, evaluated, and communicated across athenahealth’s clinical products. The Senior Product Manager will lead the AI Trust & Transparency product area, developing evaluation, governance, and transparency capabilities that support safe, effective AI experiences for clinicians, administrators, clients, and prospects. This role is based in Boston, MA and operates in a hybrid work environment. This role reports to the Product Management Director within athenaClinicals.
About the Team
The Clinical Foundations team develops shared capabilities that support athenahealth’s electronic health record products and responsible use of AI in healthcare. The team manages the Clinical AI Feature Evaluation process, including evaluation methods, performance measures, governance practices, and publication of information about AI features. Team members partner with Product, Engineering, Design, Data Science, Patient Safety, Legal, Sales, Customer Success, and Product Marketing, while working with scrum teams in Boston and India. The team uses AI evaluation and observability platforms such as Arize and Kubeflow to assess solutions built with third-party large language models and internally developed machine learning models. Essential Job Responsibilities Define the product vision, strategy, business cases, and multi-release roadmap for AI trust, evaluation, governance, and transparency capabilities. Translate clinician, administrator, client, and business needs into product requirements, including standards for accuracy, bias assessment, data quality, safety, and transparency. Lead the end-to-end product development lifecycle, including problem definition, requirements, user experience, objectives and key results, delivery, and measurement of customer and business outcomes. Serve as Product Owner for assigned scrum teams by defining epics and user stories, prioritizing the backlog, and making informed trade-offs among customer value, scope, timing, and technical constraints. Develop evaluation strategies and safe rollout approaches for AI features, including experiments, alpha testing, beta testing, and other validation methods when appropriate. Establish and monitor performance measures that provide visibility into product outcomes, risks, and opportunities for improvement. Partner with Product, Engineering, Design, Data Science, Patient Safety, Legal, and business leaders to align product decisions with healthcare safety, regulatory, and operational needs. Develop capabilities that clearly explain how AI features work, how they were developed and evaluated, and what evidence supports their appropriate use. Integrate AI-assisted research, analysis, requirements development, prototyping, and evaluation into product workflows; assess tool limitations and outputs, apply appropriate human review, and help team members use these methods responsibly. Evaluate market conditions, customer needs, product dependencies, and business value to guide prioritization and product investment decisions. Additional Job Responsibilities Collaborate with Sales, Customer Success, and Product Marketing to develop clear customer-facing product information and service descriptions. Support product teams in applying shared AI evaluation tools and governance practices to their business areas. Share product management and responsible AI practices with colleagues through coaching, documentation, and working sessions. Contribute to planning and coordination across the broader Product Management organization. Expected Education & Experience Required: At least 8 years of relevant professional experience, including 4 or more years in product management. Required: Practical experience with large language model evaluation methods, such as deterministic testing, model-based evaluation, human