AI Product Owner

A Data Science/AI Product Owner is a key role responsible for ensuring that data-driven and AI-powered products deliver tangible business value while aligning with the overall strategic vision of the organization. They act as a critical bridge between business stakeholders and technical teams, particularly the data science and engineering departments, to ensure that projects are delivered efficiently and meet business objectives.

Essential Duties and Responsibilities:

1. Product Vision and Alignment: The Data Science/AI Product Owner plays a vital role in
translating high-level business objectives into actionable data science and AI-driven solutions.
They work closely with business leaders to define the product vision and ensure that it aligns
with the organization’s strategic goals. This involves understanding business needs, identifying
opportunities where data science and AI can provide value, and prioritizing efforts based on
impact and feasibility.

2. Backlog Management and Prioritization: A core responsibility of the Product Owner is
managing the product backlog. They gather and refine business requirements, breaking them
down into clear, actionable user stories and technical tasks for the data science team. By
prioritizing features and tasks based on business value, they ensure that the data science/AI
team works on the most critical initiatives first, delivering incremental value throughout the
development process.

3. Cross-Functional Collaboration: The Data Science/AI Product Owner acts as the primary point of
contact between business stakeholders and the data science/AI team. They facilitate
communication, ensuring that business requirements are fully understood by the technical
teams and that the proposed solutions are feasible. This close collaboration ensures alignment
between business goals and technical deliverables, helping to remove any bottlenecks and
ensuring smooth execution.

4. Agile Leadership and Execution: As part of an Agile team, the Product Owner leads Agile
ceremonies such as sprint planning, daily stand-ups, sprint reviews, and retrospectives. They
work to ensure the team follows Agile best practices, delivering high-quality outputs in each
iteration. By continuously reviewing and adjusting priorities, they help keep the team on track
and adaptable to changing business needs.

5. Stakeholder Communication: An essential part of the role is communicating project progress,
results, and value back to business stakeholders. The Product Owner translates complex data
science concepts and results into clear, actionable insights that business leaders can understand
and act upon. They manage expectations, providing updates on timelines and deliverables, and
ensure stakeholders are fully informed of project outcomes and next steps.

6. Performance Metrics and Model Validation: The Product Owner is responsible for defining and
tracking key performance indicators (KPIs) to measure the success of data science and AI
models. They ensure that all models are validated, accurate, and reliable, continuously
improving the quality of predictions and insights delivered by the team. This includes working
with data scientists to monitor the performance of models in production, addressing any issues
or opportunities for optimization.

7. Ethics, Compliance, and Governance: In addition to the technical and business aspects, the
Product Owner ensures that all data science and AI initiatives comply with ethical standards,
data governance policies, and relevant regulations. They work closely with legal, compliance,
and data governance teams to ensure that data privacy and security requirements are met,
mitigating risks and ensuring responsible AI usage

Minimum Required Qualifications:

• A Bachelor's or Master’s degree in a relevant field such as Data Science, Computer Science, Engineering, or Business. Advanced degrees or certifications in data science or AI are a plus.
• Strong Understanding of Data Science/AI: A deep understanding of data science and AI methodologies, tools, and technologies is essential, including knowledge of machine learning
models, statistical analysis, and data processing techniques.
• Product Management Expertise: Experience in Agile product management, including managing backlogs, writing user stories, and working closely with technical teams. Certifications like Certified Scrum Product Owner (CSPO) are beneficial.
• Business Acumen: Ability to understand business goals and translate them into data science requirements. Strong decision-making skills, with the ability to prioritize tasks based on value and feasibility.
• Communication Skills: Excellent ability to communicate complex technical concepts to nontechnical stakeholders. Strong presentation skills and the ability to manage stakeholder expectations.
• Leadership and Collaboration: Experience working cross-functionally, leading teams, and managing the execution of data science and AI projects within an Agile framework.