Merchandiser Hand-Knit Segment

Position Overview:

We are building a new retail merchandising platform on top of that data. It answers what to stock, how much, and in which store, combining sales, catalogue, and customer data with GeoIQ’s location attributes at retail scale: thousands of stores, tens of thousands of SKUs, decisions made weekly.

As the product manager on this platform, you will own how those decisions reach the people who make them: category managers, planners and store operations teams. The models are being built alongside the product, so you will decide what gets built, in what order, and how its output fits into a buyer’s weekly planning cycle. You will work every day with data science and engineering, and what you ship changes what a store actually orders.

Responsibilities:

• Own one or more platform modules (demand forecasting, assortment planning, replenishment, new-launch allocation) from problem definition through to adoption.
• Spend time with merchandisers, category managers and store teams to understand how buying and replenishment decisions are made today, often in spreadsheets, and where that process breaks.
• Write specs that data science and engineering can build from: the decision being supported, its inputs, the business rules and constraints (minimum display quantities, pack sizes, warehouse reserves, brand rules), and what a good output looks like.
• Design the workflows that let planners act on model output with confidence: recommendation screens, overrides, explanations and exception queues.
• Define success metrics with the business (sell-through, stockouts, inventory turns, forecast accuracy, recommendation acceptance) and set up pilot-versus-control rollouts to measure them.
• Run pilots with store clusters and categories, gather feedback, and decide what changes before a module scales to the full network.
• Lead discovery when the platform extends to new brands and markets, each with its own data, supply chain and planning process.
• Keep the roadmap, priorities and trade-offs clear to merchandising, category, supply chain and leadership teams.

Expectations:

• Work experience: 2–5 years, with at least 2 years in product management
• Prior experience taking a product from problem definition to adoption by real users
• Comfortable with data: able to write SQL, work through a spreadsheet model, and read a model evaluation or A/B test result
• Clear written communication, including specs that engineers and data scientists can build from without a meeting
• Understanding of how ML-driven products differ: probabilistic outputs, accuracy limits, and the need for user overrides
• Experience with B2B, internal tools or data-heavy products will be an added advantage
• Experience in retail, e-commerce, merchandising, supply chain or inventory planning will be an added advantage
• Prior experience as an analyst, engineer or data scientist will be an added advantage

Join us early and own the product that decides what thousands of stores stock each week, working directly with the merchants who use it and the team that builds it.