Sr. Manager, Fulfillment Performance and Optimization
Samsung
- Location
- 6625 Excellence Way, Plano, TX, USA
- Work model
- On-Site
- Level
- Senior
- Posted
- Sep 1, 2026
About this role
Position
Summary This role is not eligible for immigration sponsorship Samsung Electronics America, Inc. (SEA), the U.S. Sales and Marketing subsidiary, is a leader in mobile technologies, consumer electronics, home appliances, enterprise solutions and networks systems. For more than four decades, Samsung has driven innovation, economic growth and workforce opportunity across the United States—investing over $100 billion and employing more than 20,000 people nationwide. By integrating our large portfolio of products, services and AI technology, we’re creating smarter, sustainable and more connected experiences that empower people to live better. SEA is a wholly owned subsidiary of Samsung Electronics Co., Ltd. To learn more, visit Samsung.com. For the latest news, visit news.samsung.com/us. Role and Responsibilities Delivering large, complex products to customers’ homes is among the more demanding problems in eCommerce. It spans multiple partners, fulfillment pathways, and systems. Performance is measured daily in cost, service, and customer experience. Samsung Electronics America is seeking a Sr. Manager, eCommerce Fulfillment Performance and Optimization to own the economic model of fulfillment performance. The job of this role is to translate operational performance, carrier results, and customer feedback into quantified cost. That model drives how volume is allocated across our carrier network, which operational priorities the organization pursues, and how platform investment is directed. The ideal candidate brings deep expertise in quantitative decision modeling, cost-to-serve analysis, and operational performance measurement, with experience turning complex data into decisions that move a large operation. This individual will work cross-functionally with Fulfillment Operations, and the broader logistics organization to optimize how our fulfillment network performs across cost and service. This is an analytical and modeling role, rather than a reporting or day-to-day operations role. Based in our Plano, TX office, this position carries visibility to senior leadership and significant influence over how our fulfillment network performs. Specific responsibilities include: Performance measurement and cost modeling: Own the Cost of Poor Quality (COPQ) model, converting service outcomes, damages, returns, reschedules, and customer contacts into quantified cost by driver. Partner with Data and Analytics Team to define the measurement framework for delivery promise accuracy, root cause attribution, and fulfillment path performance. Own metric definitions and attribution methodology across Fulfillment Operations. Quantify comparative performance across fulfillment paths, carriers, markets, product categories, and service levels Establish and maintain promise attainment measurement against the original customer commitment, consistent with standard supply chain reference definitions. Carrier and fulfillment path optimization: Own the analytical models behind carrier allocation strategy, including cost-to-serve normalization across paths with differing cost structures. Recommend allocation configuration changes, supported by simulation against order history, and measure realized impact following implementation. Optimize allocation within contractual volume commitments, rate tiers, and additional continually-evolving constraints. Model the cost and service profile of prospective fulfillment network changes ahead of commitment, and track the performance against that model afterward. Improvement Agenda: Maintain a single prioritized, dollar-sized view of performance drivers across all fulfillment paths and domains. Bring root cause, quantified impact, and supporting evidence to the accountable domain owner. Track initiatives to closure and verify results in the data. Validate that implemented improvements hold by verifying realized impact against the original case, and refreshing cost coefficients on a set cadence to keep the