Senior Manager, Product Management – Line Plan & Buy Plan Decision Logic

About the role

The Senior Product Manager, Line Planning & Buy Planning Decision Logic is responsible for making Gap Inc.'s line planning and buy planning applications intelligent. This role sits at the intersection of data science, product delivery, and merchandising — owning how predictive models are shaped, operationalized, and embedded into the workflows that Merchants, Buyers, and Planners rely on every day to build lines and commit to buys.

Responsibilities

• Model Integration & Product Intelligence
• Own the product strategy for embedding data science models — including demand forecasting, assortment optimization, buy quantity recommendations, and attribute-based line building — directly into Line Planning and Buy Planning application workflows.
• Partner deeply with Data Science to shape model requirements, define input/output specifications, and drive model development priorities as capabilities move from partial build to production.
• Define how model outputs are surfaced in the UI: inline recommendations, confidence indicators, explainability layers, and override mechanisms that keep users in control while building trust over time.
• Establish feedback loops between user behavior (overrides, edits, adoption rates) and model improvement — ensuring the application gets smarter with use.
• Maintain expert-level understanding of each model in scope: how it works, where it performs well, where it fails, and what business conditions affect its reliability.
• • Define and prioritize the backlog across model integration, UX, and workflow features — balancing user adoption needs with data science delivery timelines and engineering capacity.
  • Write precise user stories, model contracts, and acceptance criteria that hold up across data science, engineering, and business stakeholder reviews.
  • Lead UAT in partnership with Merchandising and Buying, designing test scenarios that validate recommendation accuracy, model explainability, and real-world usability under seasonal planning conditions.
  • Ensure production readiness for all model-driven features — including monitoring, QA protocols, and incident response for model degradation or output failures.
• Stakeholder Partnership & Adoption
• Serve as the primary product interface for Merchants, Buyers, and Planners — translating workflow needs into precise model and application requirements, and building confidence in AI-driven recommendations through rigorous delivery and transparent communication.
• Communicate model confidence levels, known limitations, and data dependencies clearly — helping business partners calibrate when and how to rely on platform intelligence.
• Drive adoption of new intelligent capabilities through training, embedded support, and change management during go-live periods.
• Represent Line Planning and Buy Planning Decision Logic in cross-functional forums, ensuring roadmap dependencies with Data Science, Data Engineering, and adjacent P2M capabilities are visible and managed.

Qualifications

• 8–12+ years of experience in product management, with meaningful depth in data-intensive or AI/ML product environments — ideally in retail, merchandising, or a related planning domain.
• Demonstrable experience owning products that embed machine learning or data science models into user-facing workflows — not just integrating outputs, but shaping how models are built, validated, and trusted by end users.
• Deep fluency partnering with Data Science teams — able to engage credibly on model design, feature engineering tradeoffs, confidence and accuracy metrics, and what it means for a model to be production-ready.
• Strong intuition for model failure modes: you know how to anticipate model drift, training data gaps, edge case degradation, and override pattern abuse — and you build products that surface and handle these gracefully.
• Experienced defining how AI recommendations are presented to business users — including explainability, confidence signaling, and override mechanics that build trust without undermining adoption.
• Highly skilled at writing precise product requirements — user stories, model contracts, and acceptance criteria — that hold up across data science, engineering, and business stakeholder reviews.
• Familiarity with retail merchandising, line planning, buying, or assortment planning processes is a meaningful plus — especially understanding how planning decisions are made across the seasonal calendar.