Logistics Engineer - Warehouse Process Specialist

Schneider Electric; Tyler (TX); On-site; USD 90,000 - 120,000; Full time

Job summary

Schneider Electric is seeking a Logistics Engineer to drive world class performance in our distribution centers, applying SPS and 4IR tools to optimize warehouse flow, layout, and governance. This role blends industrial engineering with data analytics, machine learning, and AI to translate analysis into practical on‑floor actions.

You will design and improve inbound, storage, picking, packing, and outbound processes, build dashboards (Tableau/Power BI), and partner with IT and operations to

Qualifications

• Bachelor of Science in Industrial Engineering or related quantitative field.
• Minimum of 3 years in logistics, warehousing, distribution, or manufacturing operations with demonstrable improvements.
• Proven experience improving warehouse flow, layout, and material handling using IE and data-driven methods.
• Experience designing or strengthening process control and governance systems (standard work, KPIs, SIM meetings, escalation, control plans).
• Hands-on use of Advanced Data Analytics to solve operational problems (productivity, capacity, service, inventory).
• Exposure to ML/AI concepts and tools (forecasting, clustering, anomaly detection, optimization).
• Proficiency with SQL and analytics environments (Python or C#) and BI tools (Tableau/Power BI).

Responsibilities

• Design, analyze, and continuously improve warehouse flow processes to increase throughput and service levels.
• Lead layout/design optimization using IE tools and CAD with data-driven scenario modeling.
• Strengthen governance of process controls and KPIs for stable operations.
• Use analytics, ML/AI to detect bottlenecks, analyze demand and capacity, and optimize labor planning and routing.
• Create and maintain time standards and engineered labor standards.
• Build digital dashboards and reporting to support daily management and problem solving.
• Collaborate with IT, Data, and operations to leverage IoT, WMS/WCS data, and predictive models.

Skills

Data analytics

Industrial engineering

Lean Six Sigma

Problem solving

Leadership

Education

Bachelor of Science in Industrial Engineering

Tools

SQL

Power Automate

PowerShell

PowerApps

Copilot Studio

Python

C#

Tableau

Power BI

Job description

The Logistics Engineer is a key member of the logistics warehouse team and is responsible for driving world class performance in operations, customer service, and productivity within our distribution centers. This role leads to continuous improvement using the Schneider Performance System (SPS), with a strong focus on warehouse flow optimization, warehouse design, and governance of process controls enabled by Industry 4.0 (4IR) tools and technologies.

The Logistics Engineer combines core industrial engineering skills with advanced data analytics, machine learning, and AI to understand how the warehouse actually operates, identify constraints, and implement robust, data driven solutions. The successful candidate is a hands on leader and influencer who can operate comfortably on the warehouse floor and in digital tools, translating complex analysis into practical actions.

What will you do?

• Design, analyze, and continuously improve warehouse flow processes (inbound, storage, picking, packing, outbound) to reduce waste, increase throughput, and improve service levels.
• Lead warehouse layout and design optimization using IE tools (time studies, capacity models, simulations) and CAD, supported by data driven scenario modeling.
• Strengthen and standardize governance of process controls (standard work, work instructions, Time Standards, KPIs, control plans) to ensure stable, repeatable operations.
• Use Advanced Data Analytics, ML, and AI tools to:
  • Detect bottlenecks and inefficiencies in real time.
  • Analyze demand, workload, and capacity requirements.
  • Optimize labor planning, slotting, routing, and equipment utilization.
• Conduct SMB2 time studies or utilize MTM or video based time measurement software to create and maintain accurate time standards; use these to drive engineered labor standards and productivity improvements.
• Build and maintain digital dashboards and monitoring tools (e.g., in Tableau, Power BI, or similar) that provide visibility to performance, process adherence, and exceptions for leaders and operators.
• Design and implement automated data pipelines and reporting to support daily management, problem solving, and governance of key warehouse processes.
• Identify ergonomic and safety concerns using data (near miss trends, exposure data, motion analysis) and collaborate with EHS and operations to design and implement preventive solutions.
• Apply Lean and Six Sigma methodologies, SPS tools, PFMEA, Control Plans, 8D, Fishbone diagrams, and root cause analysis, enhanced by data science and advanced analytics.
• Partner with IT, Data, and performance / smart factory teams to leverage 4IR solutions such as IoT sensors, WMS/WCS data, digital twins, and predictive models in daily operations.
• Develop business cases and cost/benefit analyses for process and capital improvements, including data driven justification for automation, technology, and layout changes.
• Ensure that new processes and tools are embedded into governance: define KPIs, alerts, standard work, SIM (Short Interval management) and update the PIP (Performance Improvement Plan) policy.
• Coach supervisors, team leaders, and operators in understanding data, KPIs, and process standards, building a culture of data driven decision making and continuous improvement.
• Collaborate with other engineers, designers, maintenance technicians, and cross functional teams to pilot innovations, validate results, and scale successful solutions.

Who will you report to?

• Distribution Engineering Manager

What qualifications will make you successful for this role?

• Bachelor of Science, preferably in Industrial Engineering, other Engineering disciplines, Supply Chain, Analytics, Data Science, or a related quantitative field.
• Minimum of 3 years of relevant experience in logistics, warehousing, distribution, or manufacturing operations, with clear examples of operational improvements delivered.
• Demonstrated experience improving warehouse flow, layout, and material handling processes using industrial engineering and data driven methods.
• Experience designing or strengthening process control and governance systems (standard work, KPIs, SIM meetings, escalation, control plans) in an operations environment.
• Hands on experience using Advanced Data Analytics to solve operational problems (e.g., productivity, capacity, service, inventory), ideally in a logistics or manufacturing setting.
• Exposure to or experience with ML/AI concepts and tools (for example: forecasting models, classification/clustering for segmentation, anomaly detection for process deviations, optimization models for routing or slotting).
• Proficiency with analytics tools such as:
  • SQL for querying and joining large datasets.
  • Inbuilt automation tools such as Power automate, PowerShell, PowerApps, Copilot Studio, and similar.
  • At least one data/analytics environment such as Python, C#, or similar.
  • One or more BI/visualization tools (e.g., Tableau, Power BI, or similar