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Product Owner Data & AI

Key Facts

Remote From: 
Category:  Product Owner
Full time
Senior (5-10 years)
English

Other Skills

  • Communication
  • Problem Solving
  • Strategic Thinking

Roles & Responsibilities

  • Bachelor’s degree in Information Science, Information Systems, Computer Science, Data Science, Business Administration, or related field (Master’s preferred)
  • 5+ years of experience in product ownership, business analysis, or project management, ideally in data, analytics, or AI-focused environments
  • Experience in higher education, research, or public sector environments strongly preferred
  • Strong understanding of data AI platforms, data lakes/warehouses, ETL pipelines, APIs, and cloud environments (e.g., Databricks, AWS, Azure, GCP, Salesforce Data Cloud)

Requirements:

  • Define and maintain the product backlog for the Data AI platform, ensuring alignment with the university’s long-term strategic plan and near-term priorities
  • Collaborate with faculty, researchers, administrators, and IT leaders to gather requirements and identify high-value data and AI use cases
  • Create, refine, and prioritize the product backlog based on business value, compliance requirements, and technical feasibility
  • Monitor product performance, collect feedback, and adjust priorities based on evolving institutional needs

Job description

POSITION SUMMARY:
The Product Owner is a member of our Enterprise Data & AI team. Product Owners work daily with teams of business stakeholders, application developers, QA testers, and Scrum Masters to deliver business value through innovative software solutions. The Product Owner is the primary product specialist for the development scrum team, manages the team’s backlog, participates actively in all phases of development, and is a key stakeholder in product related decisions and release planning.

Responsibilities
  1. Product Vision & Strategy
  • Define and maintain the product backlog for the Data & AI platform, ensuring alignment with the university’s long-term strategic plan and near-term priorities.
  • This is a Remote (work from home) position.
  • Translate institutional goals (student success, research advancement, operational efficiency) into actionable product objectives.
  1. Stakeholder Engagement
  • Collaborate with faculty, researchers, administrators, and IT leaders to gather requirements and identify high-value data and AI use cases.
  • Serve as the primary liaison between business stakeholders and the technical development team.
  1. Backlog & Prioritization
  • Create, refine, and prioritize the product backlog based on business value, compliance requirements, and technical feasibility.
  • Define acceptance criteria and ensure clarity for data, AI, and integration-related user stories.
  1. Delivery & Execution
  • Collaborate with data engineers, AI/ML specialists, developers, and architects to deliver platform features and enhancements.
  • Support Agile ceremonies (sprint planning, standups, reviews, retrospectives) and ensure the team has a clear understanding of the overall vision and business value of priorities.
  1. Governance & Data Stewardship
  • Partner with institutional data governance groups to enforce data security, compliance, and stewardship and quality standards and processes.
  • Ensure AI applications comply with ethical and responsible AI guidelines in higher education.
  1. Analytics & AI Enablement
  • Guide the development of AI/BI apps, BI apps, self-service analytics, and AI-powered insights for faculty, staff, and leadership.
  • Identify opportunities to embed AI/ML models (e.g., student retention predictions, enrollment forecasting, research analytics).
  1. Change Management & Adoption
  • Communicate platform benefits, features, and updates to stakeholders.
  • Drive adoption through demonstrations, training, and user enablement activities.
  • Help guide value creation, realization, and measurement through Data & AI products
  1. Continuous Improvement
  • Monitor product performance, collect feedback, and adjust priorities based on evolving institutional needs.
  • Stay current on data & AI/ML trends and recommend platform innovations.
 
Qualifications
 
Education & Experience
  • Bachelor’s degree in Information Science, Information Systems, Computer Science, Data Science, Business Administration, or related field (Master’s preferred).
  • 5+ years of experience in product ownership, business analysis, or project management, ideally in data, analytics, or AI-focused environments.
  • Experience in higher education, research, or public sector environments strongly preferred.
  • Proven track record of helping deliver enterprise grade data & AI platforms and products within SAFe Agile environments. 

Technical Knowledge
  • Strong understanding of data & AI platforms, data lakes/warehouses, ETL pipelines, APIs, and cloud environments (e.g., Databricks, AWS, Azure, GCP, Salesforce Data Cloud).
  • Working knowledge of Data Governance (e.g. Purview) and Master Data Management (e.g. Profisee) tools.
  • Familiarity with AI/ML concepts, predictive analytics, and responsible AI frameworks.
  • Working knowledge of BI/analytics tools (e.g., Tableau, Power BI, Salesforce CRM Analytics, Looker).
  • Experience with data governance, privacy, and compliance (FERPA, HIPAA, GDPR, etc.).

Skills & Competencies
  • Strong product management skills: backlog prioritization, user story creation, acceptance criteria.
  • Excellent stakeholder engagement and communication skills—able to translate technical concepts into business value.
  • Strategic thinker with ability to align platform initiatives to institutional goals (student success, research excellence, operational efficiency).
  • Skilled in Agile/Scrum methodologies, Jira/Azure DevOps or similar tools.
  • Strong problem-solving, analytical, and decision-making skills.
  • Ability to drive adoption and change management in diverse academic and administrative communities.

Preferred Certifications
  • Certified Scrum Product Owner (CSPO) or SAFe Product Owner/Product Manager.
  • Data or cloud certifications (e.g., Databricks, Azure Data Fundamentals, Azure Purview, Azure ADO, Profisee).
  • AI/ML or analytics certifications (e.g., Databricks MLFlow or equivalent).

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