Product Owner, AI

Ovative Group•Minneapolis, MN

1d•$90,000 - $132,000•Hybrid

About The Position

Ovative is building on the foundation of EMRge, our marketing intelligence platform, and growing a portfolio of client-facing products in production. EMRge is supported by a mature data platform and product and engineering capabilities that are now being extended to serve a broader AI operating model across the company. This includes governed operational data, a managed integration and tooling layer, and a repeatable path from a real business problem to a production AI capability with a clear owner, security review, and quality bar. With an AI Center of Excellence active in every function within the organization and more than 600 people across media, measurement, analytics, and strategy being asked to work differently, this role will help build that next layer. You will own the backlog for this work. You will find the highest value problem underneath each request, turn it into work engineers can execute, and sequence it so each release builds on the last. You will set the roadmap, decide what ships first, and bring stakeholders along with the reasoning. You will report directly into the AI Lead. This is a deeply embedded execution role. You will need to understand the mechanics of agentic AI systems well enough to write meaningful acceptance criteria, recognize when a data model, integration, or tooling decision has product implications, and communicate tradeoffs clearly to both engineers and business stakeholders. You will not build the agents, but you must understand what they do, where they break, and why it matters. Working closely with AI Engineers, Data Engineers, IT and Security, and the functional leaders whose teams will use what we ship, you will guide the delivery of foundational data, integration, and tooling capabilities. You will ensure that solutions are grounded in real user needs, technically feasible, and scalable across teams and client accounts.

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 comfortable discussing APIs, data pipelines, schemas, and integration boundaries without requiring deep implementation expertise.
Working fluency with modern AI application patterns including LLMs, retrieval, tool use and function calling, agent design, and the practical limits of each, 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, 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 articulate the why it matters for engineering driven decisions in stakeholders can act on.
Excellent communication and stakeholder engagement abilities including executive level communication and the credibility to influence senior leaders across functions.
Experience with Atlassian tools including Confluence and Jira, and strong documentation instincts.

Nice To Haves

Experience building internal platforms or enablement products where colleagues are the users and adoption, not 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 actually gets done.
Experience standing up intake, triage, or request management processes for a team serving many internal stakeholders.
Experience with evaluation and quality practices for AI systems including building eval sets, measuring regression, and defining acceptable performance for non deterministic outputs.

Responsibilities

Translate program level objectives into well formed user stories with clear acceptance criteria that reflect 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, challenging priorities rather than executing against them, and making the call without escalating when 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.
Treat governance as part of the product. Ensure every agent and tool shipped has a named owner, an automation tier assignment, an access and identity model, and a documented review path, working with IT and Security rather than around them.
Translate technical work into business impact by articulating why a data model, tooling, or agent decision matters in terms of time recovered, quality, risk reduction, or client outcomes.
Run the team ceremonies and own the team cadence. You will act as the Scrum Lead on this team and will facilitate standups, refinement, and retros, remove blockers, and improve how the team works, partnering with engineering leads rather than handing that off.
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 real infrastructure dependencies.
Define and run the intake path for AI build requests coming from client teams, functional leads, and CoE captains, including the criteria that route a request to a self serve pattern, a rapid client specific build, or the platform roadmap.
Drive story readiness upstream so work arrives at refinement with clear problem framing, acceptance criteria, data dependencies, and security questions already identified, rather than surfacing at QA.
Facilitate refinement, sprint planning, and demos partnering with engineering leads to break work into thin vertical slices that deliver a working capability end to end rather than a 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 it reaches 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, holding your ground and communicating decisions clearly when leadership is not in the room.
Manage expectations against capacity. You will say no clearly, explain the reasoning, and point requesters to the right path.
Communicate progress and outcomes in clear, tailored updates to executive sponsors, steering leaders, and functional leadership.
Make what the team ships usable. Own the product facing documentation, build patterns, and reusable assets in Confluence, and partner with AI enablement and the AI Center of Excellence captains who carry adoption into their own teams.
Lead upstream discovery work with the internal teams who do the work, media, measurement, creative, and operations, to understand unmet needs before work is scoped, so the backlog reflects real user problems rather than tool requests.
Establish user shadowing and feedback loops as a standing practice, building direct line of sight into how work actually gets done today, where the manual effort sits, and where the operational source of truth lives.
Insist on demand side evidence. Every build on the roadmap should trace to an observed user problem with a named owner and a measurable outcome, not to an interesting capability.
Own adoption as a success measure. For an internal platform, shipped is not the finish line. Track usage, abandonment, and workflow change, and feed that back into prioritization.

Benefits

Access to all office spaces in MSP, NYC, and CHI
Frequent, paid travel to our Minneapolis headquarters for company events, team events, and in-person collaboration with teams
Generous paid vacation policy
401k match program
Top-notch health insurance options, inclusive of same sex partners
Family formation benefits including reimbursement options for fertility, pregnancy, and parenting needs
Monthly stipend for your mobile phone and data plan
Sabbatical program
Charitable giving via our time and a financial match program
Shenanigan’s Day