Sr. Manager, Fulfillment Performance and Optimization

Samsung Electronics•Plano, TX

3d•Onsite

About The Position

Samsung Electronics America is seeking a Sr. Manager, eCommerce Fulfillment Performance and Optimization to own the economic model of fulfillment performance. The role translates operational performance, carrier results, and customer feedback into quantified costs that inform volume allocation across the carrier network, operational priorities, and platform investment. It focuses on analytical modeling and decision-making rather than reporting or day-to-day operations, and works cross-functionally with Fulfillment Operations and the broader logistics organization to improve network cost and service performance. Based in Samsung’s Plano, Texas office, the position has visibility to senior leadership and significant influence over fulfillment network performance. The role is not eligible for immigration sponsorship.

Requirements

Bachelor’s degree required.
At least 8 years of relevant experience in analytical, operations, or commercial roles, with meaningful exposure to physical operations or logistics.
Demonstrated ownership of cost or service performance outcomes in an operational environment.
Proven ability to build quantitative decision models and defend the methodology and assumptions behind them.
Experience translating complex analysis into executive-level recommendations and driving decisions from it.
Strong cross-functional presence and ability to maintain credibility with operational leaders and technical teams.

Nice To Haves

Experience in supply chain, logistics, fulfillment, or eCommerce operations.
Direct experience with operations excellence, quality engineering, or continuous improvement, including DMAIC or cost of quality.
Direct experience with appointment-based delivery and installation logistics.
Background in pricing, revenue management, or another discipline requiring quantified trade-off analysis.
Advanced degree in Analytics, Supply Chain, Engineering, or a related quantitative field.

Responsibilities

Own the Cost of Poor Quality (COPQ) model, translating service outcomes, damages, returns, reschedules, and customer contacts into quantified costs by driver.
Partner with the Data and Analytics Team to define measurement frameworks 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.
Own analytical models behind carrier allocation strategy, including cost-to-serve normalization across paths with differing cost structures.
Recommend carrier allocation configuration changes using simulations against order history, and measure realized impact after implementation.
Optimize allocation within contractual volume commitments, rate tiers, and evolving constraints.
Model cost and service profiles of prospective fulfillment network changes before commitment, then track performance against the model.
Maintain a prioritized, dollar-sized view of performance drivers across fulfillment paths and domains.
Present root causes, quantified impact, and supporting evidence to accountable domain owners; track initiatives to closure and verify results in the data.
Validate that implemented improvements hold by comparing realized impact with the original case, and refresh cost coefficients on a regular cadence.
Lead a recurring fulfillment performance review with cross-functional domain owners.
Build analytical cases for platform enhancement requests, including ROI quantification for market requirements documents.
Model proposed service and pricing initiatives before commitment.
Quantify operational remediation, revenue-generating, and cost-saving initiatives on a consistent economic basis.
Validate realized impact after launch and feed results back into the model.