AI Product Owner Injaz - Artificial Intelligence MIT

Key Result Areas
Product Strategy & Roadmap
- Define the vision, strategy, and roadmap for AI products in alignment with organizational goals.
- Translate business needs into AI product requirements and prioritize features based on value and feasibility.
- Continuously assess market trends, customer feedback, and emerging AI technologies to inform product evolution.
Backlog Management & Delivery
- Own and manage the product backlog, ensuring clarity, prioritization, and alignment with sprint goals.
- Collaborate with engineering and data science teams to ensure timely delivery of high-quality AI features.
- Define acceptance criteria and validate deliverables against business objectives.
Stakeholder Engagement
- Act as the primary point of contact for business stakeholders, ensuring alignment on priorities and expectations.
- Communicate product vision, progress, and outcomes to executives and cross-functional teams.
- Facilitate workshops and demos to gather feedback and drive adoption.
Governance & Compliance
Ensure AI products comply with internal governance frameworks, regulatory requirements, and responsible AI principles.
- Partner with AI Governance, Risk, and Security teams to embed controls for fairness, transparency, and explainability.
Performance & Value Realization
- Define KPIs and success metrics for AI products; monitor performance and adoption.
- Drive continuous improvement through data-driven insights and user feedback.

Knowledge, Skills and Experience
Education: Bachelor’s degree in Business, Computer Science, Data Analytics, or related field (MBA preferred).
Preferred experience:
- 8+ years in product management, with at least 3 years in AI/ML or data-driven products, or
- Management consulting background with experience in strategy, operating models, and transformation programs, or
- Startup experience in AI or emerging tech, demonstrating agility and innovation mindset.
Skills & Expertise:
- Strong understanding of AI/ML concepts, model lifecycle, and data platforms.
- Experience with Agile methodologies, backlog management tools (e.g., Jira, Azure DevOps).
- Familiarity with AI governance, compliance, and ethical AI practices.
Soft Skills: Excellent communication, stakeholder management, and ability to translate technical concepts into business value.