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. This is not a role for a PM who hands off to data science and waits. You will be a deep collaborator and co‑developer — fluent in how demand forecasting, assortment optimization, buy quantity, and attribute‑based line building models work, where they are in their maturity, and what it takes to translate partially‑built modeling capability into trustworthy, usable product. You will own how model outputs surface in the application, how users understand and act on recommendations, and how the system learns from override behavior and feedback. You will serve as the connective tissue between Data Science, Engineering, and the business — ensuring that the intelligence built into the platform is accurate, explainable, and adopted. What You’ll Do 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. Application Product Ownership 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. Who You Are 10+ 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. Fluent in translating between technical and business language: equally at home in a model review with Data Science and a seasonal planning session with Buyers or Merchants. Proven track record of shipping model‑driven product features in agile environments — managing backlog, sprint execution, UAT, and production readiness with rigor and accountability. Comfortable with ambiguity and able to make sound prioritization calls when model maturity, data quality, and delivery timelines are in tension. Strong written and verbal communicator who brings transparency to model limitations, data dependencies, and delivery tradeoffs without losing the trust of business or technical partners. 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. Bachelor’s degree in Business, Analytics, Data Science, Computer Science, or a related field; advanced degree or equivalent experience a plus. #J-18808-Ljbffr Dormont Manufacturing Co