Senior Product Owner Consultant (Hybrid)

Requirements

• Bachelor's degree in Business, Technology, Engineering, or related field
• 7+ years of experience as a Product Owner, Product Manager, or similar role in an agile environment
• Experience delivering data, analytics, or AI/ML-driven products in production environments
• Strong understanding of agile methodologies (Scrum, Kanban) and product lifecycle management
• Experience working with cross-functional teams including engineering, data science, and business stakeholders
• Ability to define and manage both functional and non-functional requirements
• Strong stakeholder management and communication skills
• Experience working in enterprise environments with governance and compliance considerations
• Familiarity with AI/ML concepts, data pipelines, and model lifecycle is strongly preferred
• Experience in AI/ML product delivery or AI transformation initiatives
• Experience working within a Center of Excellence (CoE) model
• Familiarity with cloud platforms (AWS, Azure, GCP) and modern data architectures
• Experience enabling or coaching teams in agile delivery practices.
• Experience defining KPIs and measuring business value realization.
• #LI-Hybrid **This position will have a hybrid working arrangement, working in-office for a minimum of 3 days a week.**
• The estimated base pay range for this job is:
• $111,300.00 - $207,800.00
• Pay may vary depending on job-related factors and individual experience, skills, knowledge, etc. More information on base pay and incentive pay (if applicable) can be discussed with a member of the Se

Additional Information

*Securian Financial Groups internal position title is Product Owner Senior Consultant. Position Summary: The Senior Product Owner Consultant will lead discovery and delivery across AI Mini-Pods, driving the execution of scalable, production-ready AI use cases within a 3-4 month delivery window. This role operates at the intersection of business, technology, and data, ensuring AI solutions are aligned to enterprise standards, deliver measurable business value, and are positioned for long-term adoption. Responsibilities include but not limited to: AI Discovery & Use Case Definition Lead structured discovery workshops with business stakeholders. Validate problem statements and define measurable business outcomes and KPIs. Translate business objectives into scalable AI solution scope. Identify data dependencies, technical constraints, and integration points. Define clearly bounded, production-ready use case scope aligned to a 3-4 month delivery timeline. Product Ownership & Backlog Management Define and maintain a comprehensive product backlog including: Business requirements. AI/ML capabilities. Non-functional requirements (security, performance, explainability, compliance). Acceptance criteria tied to business outcomes. Prioritize backlog based on business value, feasibility, and dependencies. Continuously refine and groom backlog across concurrent mini-pods. Structure work to support experimentation (e.g., technical spikes, model evaluation cycles). Agile Delivery Leadership Lead agile ceremonies including sprint planning, backlog refinement, sprint reviews, and retrospectives. Act as de-facto Scrum Master in early phases of delivery. Track sprint goals, delivery progress, risks, and impediments. Coordinate cross-functional and cross-team dependencies. Ensure delivery outcomes align with defined business value and KPIs. Enterprise Alignment & Governance Ensure alignment with enterprise AI architecture, governance, and risk standards. Partner with architecture, data governance, and security teams. Identify and mitigate risks related to: Data quality and availability. Model performance and reliability. Bias, fairness, and explainability. Operationalization, monitoring, and lifecycle management. AppDev Partnership & Transition Build strong partnerships with business-aligned application development teams. Align backlog, architecture, and delivery approach with downstream systems. Define and execute structured handoff and transition plans. Ensure documentation and knowledge transfer enable long-term maintainability. Drive adoption of AI CoE standards, patterns, and best practices. Capability Building & Reusable Frameworks Establish lightweight, outcome-driven agile practices for short AI delivery cycles (5-7 sprints). Develop reusable templates, playbooks, and delivery artifacts including: Discovery frameworks Backlog structures Sprint reporting standards AI use case playbooks Coach team members to take on Scrum Master responsibilities Enable progressive ownership transfer to AppDev teams by final sprint

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