Lead Data Scientist - Supply Chain Network Planning Design (applied ML) | Target (R0000476520)
Target lists an open Lead Data Scientist - Supply Chain Network Planning Design (applied ML) position in Target Tech, job ID R0000476520. The employer identifies 1000 Nicollet Mall in Minneapolis, Minnesota, and also 100 Mathilda Place in Sunnyvale, California. The role is full-time and described under Target's Hybrid / Flex for Your Day approach; no fully remote arrangement is established. The advertised annual pay range is USD 132,000-286,000, with an application deadline of November 19, 2026. The role builds optimization, simulation, digital twin, forecasting, capacity and scenario-planning models to guide long-term supply chain network architecture and investment decisions. It also emphasizes model governance, reproducibility, testing, documentation and auditability. The official Apply route was checked on October 19, 2026, without submitting an application; that is a verification date, not an asserted original posting date. Apply: https://target.wd5.myworkdayjobs.com/targetcareers/job/1000-Nicollet-Mall-MinneapolisMN-55403-2542/Lead-Data-Scientist---Network-Planning-and-Design--applied-ML--optimization--simulation-_R0000476520/apply
Illustrative aerial photograph of a logistics facility, not a Target site. Photo: Marcin Jozwiak / Pexels. Source: https://www.pexels.com/photo/aerial-view-of-the-distribution-center-drone-photography-of-the-industrial-logistic-zone-14060483/ - License: https://www.pexels.com/license/
Target's hiring signal points to an architecture question for strategic supply chain planning: predictive models, optimization and digital twins only matter when they can inform defensible long-term network and capital-allocation decisions.
For supply chain leaders, the challenge is not to build more simulations. It is to compare mutually constrained choices about network footprint, inventory positioning, transport flows and capacity under credible demand and cost scenarios. The condition for value is a reproducible decision model with explicit assumptions, sensitivity tests and traceable links between scenarios, recommendations and investment choices.
The main failure mode is false precision: a sophisticated digital twin that appears to validate a multiyear investment while hiding uncertain demand, capacity or operating assumptions. Target explicitly seeks governance, auditability and model validation, but the vacancy establishes hiring intent rather than proven returns or a newly deployed platform.