The ML Product Owner for the ML Insights Team is the business champion, product owner, and process lead for our machine learning insight initiatives. This role bridges the gap between data science and business adoption, driving intake and ROI discipline, portfolio and lifecycle management, stakeholder alignment, and scaled deployment of models that deliver measurable impact across the organization.

The ML PO partners with data scientists, engineering leads, business stakeholders, commercial leaders, and clinical operations to translate model outputs into business value, run a consistent project intake and delivery process, manage the data science project portfolio, and secure ongoing investment and sponsorship. 

This position requires strong data and statistical literacy, familiarity with data engineering / data warehousing concepts, product and process discipline (including a lightweight scrum-master function), and the stakeholder management skills to drive consensus in ambiguous, high-stakes environments where business teams may not yet understand AI's potential or have legacy skepticism. 

Deep hands-on ML technical ability (building or tuning models, reading research papers) is not required — the team will support the PO on technical depth.

• Own project intake: ask questions to surface the real business problem and requirements, and assess whether ML/statistical modeling is the right solution before it enters the pipeline.
• Act as a buffer against scope creep and ad hoc requests that bypass the intake process; set realistic expectations with stakeholders about ML project uncertainty and timelines.
• Ensure ROI is estimated at intake and revisited post-launch to confirm actual impact; craft business cases and ROI narratives to secure sponsorship and funding.
• Define and maintain a consistent process for the project lifecycle: intake, ROI, Business Review Documents (BRD), roadmaps, timelines, documentation, and monitoring.
• Own the ML Insights portfolio and roadmap; balance experimentation, model maturity, adoption readiness, team capacity, business value, and regulatory/clinical compliance requirements when prioritizing work.
• Play a lightweight scrum-master role: lead and coordinate meetings (stakeholder syncs, internal planning/brainstorming), keep the team on track, translate roadmaps into Jira tickets, and maintain the backlog.
• Own product lifecycle management from inception through sunset, including end-to-end monitoring and reporting of results, version adoption tracking, user feedback capture, and continuous improvement.
• Manage stakeholder engagement and communications; overcome skepticism, objections, and resistance through targeted education, demos, pilots, and case studies.
• Develop product requirements, acceptance criteria, and success metrics in partnership with data scientists; ensure models address real business needs with clear, measurable outcomes.
• Build and maintain project documentation (overviews, decks, BRDs, plans, roadmaps, timelines, status updates, technical docs) and user-facing materials (guides, training, knowledge base).
• Conduct regular stakeholder business reviews; present adoption metrics, business impact, roadmap progress, and required support or investment decisions.
• Monitor competitive landscape and external AI model innovation; identify and evaluate opportunities for vendor partnerships, feature licensing, or platform integrations.
• Ensure compliance with regulatory, audit, and data governance requirements across model development and deployment; partner with Legal, Compliance, and Data Management.
• Develop financial models and track product performance (adoption rates, user engagement, cost per transaction, ROI, etc.); provide regular reporting to leadership.
• Develop deep fluency in both the business domain and the team's technical workflow.
• Support and comply with the company's Quality Management System policies and procedures.
• Maintain regular and reliable attendance.
• Demonstrate an inclusion mindset and model these behaviors within the organization.

• Bachelor's degree in Business, Data Science, Computer Science, Engineering, or a related field; or equivalent professional experience.
• 5+ years of product owner / product management experience, including at least 2+ years working with data science, ML, or analytics products/portfolios.
• Demonstrated experience launching and scaling products from pilot through production; comfort with early-stage, ambiguous product problems.
• Solid understanding of data and statistics; able to reason about model quality, evaluation, and limitations without needing to build models hands-on or read ML research papers.
• Working knowledge of data engineering / data warehousing concepts (how datasets and warehouses are built) sufficient to communicate data needs and requirements clearly.
• Proven experience running a project intake process — surfacing the real problem behind a request, and assessing fit for an ML/statistical solution.
• Experience owning end-to-end project lifecycle process: ROI estimation, BRDs, roadmaps, timelines, documentation, and monitoring/reporting.
• Experience with lightweight agile facilitation (scrum-master-style): running planning and sync meetings, managing a backlog, translating roadmaps into tickets (e.g., Jira).
• Proven ability to articulate technical concepts and tradeoffs in business terms; create compelling ROI narratives and business cases for executive audiences.
• Experience managing cross-functional adoption and go-to-market initiatives; ability to influence stakeholders without direct authority.
• Systems thinking — able to see how a new request connects to existing projects and portfolio priorities, rather than treating everything as a one-off.
• Strong communication and active listening skills; able to identify the real underlying problem, not just the stated request.
• Comfortable with ambiguity; organized, detail-oriented, and proactive.
• User empathy — keeps the end user and end application in view as the goal.

Preferred Qualifications:

• Background or hands-on experience in data engineering, analytics engineering, or ML operations; ability to partner effectively with technical teams.
• Experience navigating sales cycles, negotiating contracts, or supporting commercial teams to integrate ML products into go-to-market strategy.
• Exposure to regulated environments, compliance requirements, or quality management systems (QMS).
• Track record of building internal advocacy or overcoming organizational resistance to new technology adoption.
• Proficiency with product analytics tools or SQL for self-service data analysis.
• Experience with Jira, Confluence, or similar collaboration and project management tools.