AI & Machine Learning Product Team Lead

Children's Healthcare of Atlanta

About The Position

The Artificial Intelligence and Machine Learning Product Team Lead owns the product lifecycle for Artificial Intelligence/Machine Learning (AI/ML) and advanced analytics initiatives across the pediatric health system - from use case discovery and prioritization through requirements definition, development, implementation, evaluation, and post-launch iteration. This role partners with stakeholders including clinicians, informaticists, as well as leaders of operational, quality, analytics and AI programs to define problems, success metrics, evaluation plans, and workflow adoption requirements. The role leads a team of AI/ML engineers and collaborates closely with business systems and electronic health record (EHR) application teams to deliver and evolve a portfolio of AI/ML products that improve outcomes for kids, families, clinicians, and the organization. For products in the portfolio, the Team Lead maintains a transparent roadmap and backlog, coordinates governance inputs (privacy, security, clinical safety), and enables efficient Agile delivery in partnership with ML engineering and EHR/application teams. The role participates in AI governance forums and, with the AI Program and Advanced Analytics Managers, communicates product portfolio status, risks, and realized impact to system leadership.

Requirements

4 years of experience in product management, analytics product ownership, healthcare analytics delivery, or data/ML program delivery roles
2 years of experience owning a backlog/roadmap and leading cross-functional delivery (analytics/engineering/clinical stakeholders)
Demonstrated experience taking analytics/AI solutions from discovery to build and workflow adoption with measurable outcomes
Bachelor’s degree in Information Systems, Data Science, Computer Science, Industrial Engineering, Analytics, or related field
Strong stakeholder management across clinical, operational, and technical teams
Ability to write clear product requirements (PRDs), user stories, and acceptance criteria
Ability to translate clinical and operational healthcare workflows into implementable requirements and adoption plans (training, comms, feedback loops)
ML literacy sufficient to guide tradeoffs (feasibility, evaluation metrics, risk/limitations) in model selection choices
Data literacy (SQL or equivalent) and comfort with defining and tracking outcome metrics
Ability to facilitate effective Agile planning while minimizing ceremony and maximizing delivery

Nice To Haves

Master’s degree preferred or equivalent experience
Experience working with healthcare data, HIPAA/privacy constraints, and governance processes
Experience partnering with clinical informatics and EHR teams (Epic preferred)
Familiarity with model governance concepts (intended use, limitations, validation expectations, monitoring requirements)
Agile product ownership experience, familiarity with Scrum

Responsibilities

Discover and prioritize high-value opportunities that align with AI program and system priorities
Partner with clinical and operational leaders to identify and prioritize AI/ML opportunities using value, feasibility, and risk criteria.
Define problem statements, target users, workflow context, and measurable success metrics.
Define product success metrics and ensures measurement plans are in place before kick-off.
Convert business and clinical needs into a clear roadmap, backlog, and delivery plan
Own the AI/ML roadmap and backlog; write epics/user stories; define acceptance criteria and release goals.
Facilitate lightweight Agile planning (sprint planning, reviews, retrospectives) focused on outcomes and unblocking.
Coordinate dependencies across data engineering, ML engineering, informatics, and EHR or business system application teams.
Ensure governance, privacy, and clinical safety requirements are embedded into each product
Coordinate privacy/security, clinical safety, and data governance inputs into delivery plans and documentation.
Ensure solutions have clear intended use, limitations defined, evaluation approach, and post-launch review expectations.
Drive workflow adoption, measure outcomes, and continuously improve products
Lead workflow adoption planning (training, change management, feedback channels) with clinical and business partners.
Monitor product success metrics post-release and drive iteration based on outcomes and stakeholder feedback.
Lead and develop AI/ML team while removing friction and protecting deep work time
Performs all aspects of leading a team including training, developing, directing work and processes, monitoring performance, and recognizing employees.
Take on the necessary intake, planning, and stakeholder alignment work to minimize context switching and protect time for the technical team.
Mentor analysts/data scientists in product thinking, documentation rigor, and stakeholder communication.