Product Owner, EMRge MMM

About the Role: 

Ovative’s Modern MMM+ is our core marketing measurement product — a proprietary Marketing Mix Modeling platform. As Product Owner for Modern MMM+, you will sit at the intersection of Data Science, Data Engineering, Client Delivery, and Client Teams — owning the backlog, shaping the delivery cadence, and translating complex modeling and measurement requirements into well-formed, executable work. You will report to the Director of Product Management and operate within our SAFe agile framework and PI-based planning cadences. 

This is a deeply embedded execution role. You will need to understand the mechanics of Bayesian MMM at a level sufficient to write meaningful acceptance criteria, recognize when a modeling or pipeline decision has product implications, and communicate tradeoffs clearly to both engineers and business stakeholders. You will not run the models — but you must understand what they produce and why it matters. 

Working closely with Data Scientists, Data Engineers, and Client Delivery leads, you will guide the delivery of foundational modeling, pipeline, and tooling capabilities — ensuring solutions are grounded in real user needs, technically feasible, and scalable across clients and verticals. 

Responsibilities of a Product Owner: 

Product Ownership 

• Translate program-level features and objectives into well-formed user stories with clear acceptance criteria that reflect business value and technical constraints — including modeling methodology decisions, pipeline changes, and tooling improvements. 

• Work closely with Data Science and Data Engineers during iterations to clarify business and functional requirements and make timely decisions on scope, data inputs, and acceptance conditions. 

• Own delivery-level tradeoff decisions (scope, sequencing, and technical constraints) — challenging priorities rather than simply executing against them, and making the call without escalating to the PM when not required. 

• Accept or reject completed stories based on acceptance criteria and the team’s Definition of Done, ensuring that model outputs, pipeline deliverables, and tooling features truly meet user needs. 

• Translate technical work into business impact — articulating why a modeling, pipeline, or tooling decision matters in terms of client outcomes, efficiency, or product quality, not just technical completeness. 

• Ensure alignment between team deliverables and program-level objectives and roadmap, raising risks and tradeoffs when needed. 

• Collaborate daily with the Scrum Lead to remove blockers, refine plans, and improve the team’s flow and ways of working. 

Backlog Management & Delivery 

• Own and prioritize the Modern MMM+ backlog across modeling, pipeline, tooling, and enablement workstreams, sequencing data science, data engineering, and full stack work to maximize delivery throughput within available capacity and technical constraints each PI. 

• Drive story readiness upstream — ensuring stories arrive at refinement well-defined with clear problem framing, acceptance criteria, and dependencies identified, rather than requiring catch-up at QA. 

• Lead sprint refinement, and demos, partnering with data science and engineering leads to break work into deliverable increments, clarify scope, and remove ambiguity so teams can reliably deliver on commitments each iteration. 

• Define acceptance criteria and partner with the team on testing and validation to ensure that modeling, pipeline, and tooling features meet quality standards and client expectations. 

• Champion MVP thinking and iterative delivery, using experimentation and feedback loops to validate modeling or tooling assumptions quickly and scale what works. 

• Maintain familiarity with the end-to-end MMM lifecycle — EDA → data harmonization → prior generation → model fitting → model lock → push to production → insights — sufficient to sequence work and identify cross-team dependencies. 

Stakeholder Management & Community Building 

• Serve as the primary point of contact for the Modern MMM+ pod with data science leads, data engineering, client delivery, and client teams — holding your ground and communicating decisions clearly even when the PM is not in the room. 

• Facilitate cross-product team dependency conversations on shared infrastructure, dependencies, and cadence standards. 

• Communicate PI progress and sprint outcomes in clear, tailored updates to executive sponsors, steerco leaders, and portfolio leaders. 

• Enable teams through training, documentation, and change management, including support for the MMM training series and internal Confluence documentation for the Modern MMM+ pod. 

User Research & Discovery 

• Co-Lead upstream discovery work — engaging with Client Delivery, Managed Services, and client teams to understand unmet needs before work is scoped, ensuring the backlog reflects real user problems and not just delivery requests. 

• Establish user shadowing and feedback loops as a regular practice, building a direct line of sight into how MMM outputs and tooling are used in client-facing workflows. 

• Balance execution with upstream problem definition, ensuring that sprint work is grounded in a well-articulated user problem, not just a feature request or technical task. 

Requirements: 

• 5–8+ years of experience in product management, product ownership, business analysis, or a related role, with at least 2 years focused on data, analytics, or measurement products. 

• Proven success owning a technical product backlog and leading delivery in a SAFe or similar scaled agile environment, including sprint planning, refinement, cross-team dependency management, and PI planning. 

• Demonstrated ability to make and communicate independent prioritization decisions — including trade-offs under pressure — without requiring PM escalation for routine decisions. 

• Demonstrated experience working closely with Data Science and Data Engineering teams on complex modeling or pipeline products — comfortable discussing data pipelines, model inputs/outputs, and schema-level decisions without requiring deep implementation expertise. 

• Strong analytical, strategic thinking, and problem-solving skills, including comfort with cost–benefit analysis, prioritization frameworks, and scope negotiation under capacity constraints. 

• Ability to translate technical work into clear business impact — articulating the “why it matters” for engineering-driven decisions in terms stakeholders and clients can act on. 

• Excellent communication and stakeholder engagement abilities, including executive-level communication and the ability to translate technical modeling or pipeline concepts into clear business value. 

• Demonstrated ability to influence and align cross-functional teams across DS, DE, and client delivery functions. 

• Experience with Atlassian tools (Confluence, Jira) and strong documentation instincts; familiarity with JSM or operational request management tooling a plus. 

Preferred 

• Background in marketing measurement, media analytics, or adjacent fields (e.g., attribution, incrementality testing, marketing mix modeling, or media planning). 

• Familiarity with Bayesian modeling concepts — including priors, ROAS, diminishing returns, and model calibration — at a conceptual, not implementation, level sufficient to evaluate feasibility and write acceptance criteria. 

• Experience with data platforms and pipeline tooling such as BigQuery, Dagster, Databricks, or similar environments. 

• Prior exposure to product configurability frameworks, feature flag taxonomy design, or tiered offering management across client segments. 

• Comfort working in environments where methodology, tooling, and process are simultaneously evolving, with a bias toward structured documentation and decision anchors. 

• Experience conducting or facilitating user research, discovery sessions, or shadowing to ground product decisions in observed user behavior.