Remote Python Data Analysis Jobs in Canton, MO (NOW HIRING)

Reporting to the Continuous Improvement Manager, ZLA, you will lead the implementation and effectiveness of Operational Data Management tools and Focused Improvement impact across our manufacturing network. You will ensure alignment on enterprise-level goals by identifying loss opportunities, promoting cross-site collaboration, and supporting the implementation of improvement strategies. You will serve as a resource for multiple facilities, help build site-level capabilities, and contribute to system-wide optimization efforts.

A Taste of Your Responsibilities

• Partner with manufacturing sites across the network to identify systemic loss opportunities and execute targeted Focused Improvement projects aligned with corporate objectives.

• Collaborate with plant teams to standardize processes and share best practices across the manufacturing network.

• Analyze multi-site operational data to identify trends, chronic losses, and improvement opportunities.

• Lead Operational Data Management tools, ensuring data is accurate, reliable, and actionable.

• Lead cross-site technical projects focused on line performance, throughput variability, labor optimization, and operational efficiency.

• Apply Define, Measure, Analyze, Improve, Control, statistical process control, and design of experiments methodologies to drive data-informed improvements.

• Support the deployment and sustainability of Focused Improvement tools, including Unified Problem Solving, Lean, and Six Sigma methodologies.

• Align key performance indicators across facilities to support enterprise-wide consistency and performance goals.

• Provide coaching and hands-on support to site teams using Value Stream Mapping, Loss Tree development, root cause analysis, and other improvement tools.

• Partner with Autonomous Maintenance, Planned Maintenance, and other Manufacturing Excellence pillars to coordinate strategies and eliminate duplication of effort.

• Facilitate collaboration across sites to drive systemic improvements and knowledge transfer throughout the network.

• Develop and maintain enterprise standards for process optimization, routing, and time studies.

• Support the creation of continuous improvement playbooks, toolkits, and standard work.

• Collaborate with senior leadership to evaluate network-wide capital investment opportunities and provide data-driven business justifications.

Ingredients Required for Your Success

• Bachelor's degree in Engineering, Manufacturing, or a related field.

• 5+ years of experience in manufacturing, continuous improvement, industrial engineering, or related operational excellence roles.

• Experience supporting multiple manufacturing locations or working within a corporate manufacturing environment.

• Hands-on experience with Lean, Six Sigma, Unified Problem Solving, and continuous improvement methodologies.

• Proficiency with Power BI, Databricks, and advanced Microsoft Excel for analysis and visualization.

• Experience leading cross-functional teams and influencing stakeholders across a manufacturing network.

• Knowledge of Define, Measure, Analyze, Improve, Control, Value Stream Mapping, statistical process control, and root cause analysis methodologies.

• Project management experience within a matrixed organization.

• Willingness to travel 70% or more to manufacturing facilities across the network.

• Strong communication, facilitation, and change leadership skills.

Physical Requirements

More information about essential tasks and the facility will be shared during the hiring process. This role may require you to be in our manufacturing facilities. When visiting these sites, the physical requirements below may apply: You may be required to walk around the facility, climb stairs/ladders, kneel, or perform repetitive tasks that require hand-eye coordination. You may be required to lift or carry materials that can vary in weight and size. This is a manufacturing environment with large, moving equipment. Noise levels can be high, and temperatures may vary.

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