Sr Should Cost Engineer

Who are we?

As a global specialist in energy management, Schneider Electric takes on the challenge of transforming the way people and organizations use energy. We make electricity available for both individuals and organizations, ensuring reliability and safety thanks to our wide range of electrical distribution, automation, and critical power & cooling solutions. We provide energy‑efficiency technologies that help our customers achieve greater productivity while consuming fewer resources. Today, industries, infrastructure, and buildings account for more than 70% of global energy consumption, and with our solutions, customers can reduce energy usage by up to 30%.

Schneider Electric continues to demonstrate strong performance and global scale. In 2025, the company crossed 40 billion in annual revenues for the first time, achieving 40.2 billion with 8.9% organic growth, and has more than 140,000 employees in over 100 countries.

What are we looking for?

We are seeking a highly motivated and technically strong Senior Should Cost Engineer to enhance Schneider Electric's cost competitiveness across the North America region. In this role, you will conduct Should Cost analyses across fabricated components and raw material categories, enabling data‑driven negotiations, cost transparency, and continuous productivity improvement in addition to lead new Should Costing Digital platforms (NAM).

This role is based in Monterrey, Mexico, reporting operationally to the North America Procurement Director (Nashville, TN, USA), and dotted line with Sr Should Cost Manager (Apodaca, NL, MX). In addition to collaborating in a global matrix with cross‑functional teams.

Responsibilities:

Strategic Cost Leadership

• Develop, lead, and execute the Should Cost strategy for direct materials procurement strategies.
• Create cost-saving road maps aligned with regional and category management strategies.
• Provide insights and technical guidance as a strategic partner in sourcing and negotiation activities.
• Function as Subject Matter Expert for Digital Costing platforms and lead efforts towards successful adoption for NAM.

• Conduct detailed Should Cost and Clean Sheet analyses for key categories such as stampings, sheet metal and plastics.
• Build, calibrate, and refine cost models using analytics tools and engineering software (e.g., aPriori, Creo, Lantek, AI-enhanced platforms)
• Benchmark internal and external cost data to identify productivity opportunities.

• Work closely with Category Management, Procurement Management, and technical experts to drive gap analysis and implement corrective actions plans.
• Lead training sessions, workshops, and capability-building initiatives to increase Should Cost adoption across the procurement organization.
• Support Value engineering initiatives by driving cost optimization with key products and strategic suppliers.

• Enhance Should Cost and Clean Sheet coverage across categories
• Partner with procurement negotiators and collaborate with suppliers to achieve sustainable, fact-based cost reductions.
• Contribute to margin expansion by lowering direct material costs through analytics-driven insights.

• Self-driven / motivated individual with strong communication, collaboration and influence skill set.
• 5-10 Years of experience in Should Costing Engineering & Methodology analysis
• Strong Procurement and strategic sourcing background with proven track record of success. Negotiating skills and experience working in a strategic environment.
• Cost Structure Expert (Value Add, Raw Material, Labor Rates, Machine Rates, Overhead, SG&A)
• Demonstrated Should Cost calculation experience on Fabricated Components and Raw Material categories and certification on Engineering Software such as aPriori, Creo, Lantek, and others recent AI-enhanced platforms.
• Strong Analytical Skills and Excel proficiency for large datasets, curiosity to innovate new formulas for better calculation.
• Bachelor's degree in Supply Chain, Engineering, or other related function from a recognized University. Master degree is ideal.
• Familiar with #Regression Analysis (#Minitab, R Studio). Desired experience in Machine Learning basic foundation for Regression.