Principal, Data Scientist

About the role

Merchandising Data Science builds the systems — both automated decision engines and decision support systems — that drive high-stakes merchandising decisions: how we select and curate items, manage space, plan and allocate inventory, flow inventory through the network, decide what and how much to buy, read the competitive landscape, and price.

What you'll do

• Own one hard, well-defined problem end-to-end — e.g., "how do we estimate price elasticities better?" or "how do we reduce forecast error for seasonal items a year out?"…
• Define what a good answer looks like: decision variables or recommendation logic, objective function, constraints, fallback behavior, and measurable success criteria
• Decide whether the problem calls for a fully automated decision engine or a decision support system that augments a human decision-maker, and design accordingly
• Choose and implement the right solution approach — optimization, heuristics, simulation, forecasting, causal/statistical inference
• Build the production system, not a prototype, integrating with the merchandising, planning, and platform systems it depends on

What you need

• 7–13 yrs of experience
• Machine learning
• Any graduate