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Ovative

Digital media, measurement, and creative services.

Product Owner - AI

Technical Product ManagementConfirmed live in the last 24 hours
Full-timeHybrid

About the Job

Requirements

  • 5+ years of experience in product management, product ownership, business analysis, or a related role, with at least 2 years focused on technical, data, or platform products.
  • Proven success owning a technical product backlog and leading delivery in an agile environment, including sprint planning, refinement, cross-team dependency management, and release sequencing.
  • Demonstrated ability to make and communicate independent prioritization decisions, including tradeoffs under pressure and capacity constraints, without requiring escalation for routine calls.
  • Experience working closely with engineering and data teams on complex technical products, with comfort discussing APIs, data pipelines, schemas, and integration boundaries without deep implementation expertise.
  • Working fluency with modern AI application patterns, including large language models, retrieval, tool use and function calling, agent design, and their practical limits, sufficient to evaluate feasibility and write acceptance criteria.
  • Comfort operating in ambiguity and a bias toward creating structure, documentation, and decision anchors where none exists.
  • Strong analytical and strategic thinking and problem-solving skills, including cost-benefit analysis, prioritization frameworks, and scope negotiation.
  • Ability to translate technical work into clear business impact and explain the rationale for engineering-driven decisions in terms stakeholders can act on.
  • Excellent communication and stakeholder engagement abilities, including executive-level communication and credibility to influence senior leaders across functions.
  • Experience with Atlassian tools, including Confluence and Jira, and strong documentation instincts.
  • Applicants must be legally authorized to work in the United States for any employer on a full-time basis, without current or future need for visa sponsorship.

Responsibilities

  • Translate program-level objectives into well-formed user stories with clear acceptance criteria reflecting business value, technical constraints, and governance requirements, including data model decisions, MCP and tool exposure, agent behavior, and evaluation criteria.
  • Work closely with AI Engineers and Data Engineers during iterations to clarify business and functional requirements and make timely decisions on scope, data inputs, tool boundaries, and acceptance conditions.
  • Own delivery-level tradeoff decisions across scope, sequencing, and technical constraints, challenge priorities rather than simply execute them, and make decisions without escalation when it is not required.
  • Accept or reject completed stories against acceptance criteria and the team Definition of Done, including agent evaluation results and human-in-the-loop requirements, not just functional completeness.
  • Ensure every shipped agent and tool has a named owner, an automation tier assignment, an access and identity model, and a documented review path, working with IT and Security.
  • Translate technical work into business impact by explaining why a data model, tooling, or agent decision matters in terms of time recovered, quality, risk reduction, or client outcomes.
  • Run team ceremonies and own the team cadence as Scrum Lead, facilitating standups, refinement, and retrospectives, removing blockers, and improving team processes in partnership with engineering leads.
  • Own and prioritize the AI platform backlog across canonical operational data objects, the MCP and tooling layer, unstructured data and retrieval, agent builds, and enablement, sequencing work to maximize impact and account for infrastructure dependencies.
  • Define and run the intake path for AI build requests from client teams, functional leads, and CoE captains, including criteria routing requests to a self-serve pattern, rapid client-specific build, or platform roadmap.
  • Drive story readiness upstream so work reaches refinement with clear problem framing, acceptance criteria, data dependencies, and security questions already identified.
  • Facilitate refinement, sprint planning, and demos with engineering leads, breaking work into thin vertical slices that deliver working capabilities end to end rather than one layer at a time.
  • Champion MVP thinking and iterative delivery using a mirror, validate, invert pattern for new operational data objects so adoption is proven before a system of record is moved.
  • Define how agent quality is measured and partner with engineering on evaluation sets, regression checks, and validation so agent behavior is tested against real user tasks before production.
  • Maintain familiarity with the end-to-end path from problem definition to data availability, tool exposure, agent build, evaluation, security review, deployment, and adoption, sufficient to sequence work and surface cross-team dependencies early.
  • Serve as the primary point of contact for the AI engineering team with data products, platform, IT and Security, functional leaders, and client teams, communicating decisions clearly when leadership is not present.
  • Manage expectations against capacity, clearly decline requests when needed, explain the reasoning, and direct requesters to the appropriate path.
  • Communicate progress and outcomes through clear, tailored updates to executive sponsors, steering leaders, and functional leadership.
  • Own product-facing documentation, build patterns, and reusable assets in Confluence, and partner with AI enablement and AI Center of Excellence captains who drive adoption in their teams.
  • Lead upstream discovery with internal media, measurement, creative, and operations teams to understand unmet needs before work is scoped, ensuring the backlog reflects real user problems rather than tool requests.
  • Establish user shadowing and feedback loops as a standing practice, gaining direct insight into current workflows, manual effort, and operational sources of truth.
  • Require each roadmap build to trace to an observed user problem with a named owner and measurable outcome, rather than an interesting capability.
  • Own adoption as a success measure by tracking usage, abandonment, and workflow change and feeding findings back into prioritization.

Desired Qualifications

  • Experience building internal platforms or enablement products where colleagues are the users and adoption, rather than revenue, is the primary success measure.
  • Familiarity with MCP, agent frameworks, or orchestration tooling at a conceptual and integration level.
  • Exposure to data platform and governance concepts such as Databricks, Unity Catalog, BigQuery, data contracts, or catalog and lineage practices.
  • Understanding of AI governance and security requirements, including access control, service and non-human identity, data handling, human-in-the-loop design, and audit expectations in a SOC 2 environment.
  • Background in marketing, media, or analytics services with enough context to recognize how agency work is performed.
  • Experience establishing intake, triage, or request management processes for a team serving many internal stakeholders.
  • Experience with evaluation and quality practices for AI systems, including building evaluation sets, measuring regression, and defining acceptable performance for nondeterministic outputs.

About the Company

Ovative

Digital media, measurement, and creative services.

Ovative Group helps clients achieve sustainable growth by aligning digital marketing activities with business results. It uses the Enterprise Marketing Return (EMR) framework and its EMRge platform to unify data, measurement, and creative outputs for optimizing marketing investments. The firm combines six capabilities, including media, measurement, and creative services, through both advisory guidance and hands-on campaign execution. Its goal is to prove the business value of marketing and drive growth by improving ROI across diverse, high-growth client organizations.

Company size
501-1,000
Company stage
Not listed
Total funding
Not listed
Headquarters
Minneapolis, MN
Founded
2009

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