Sr. Continuous Improvement Engineer

A growing organization is expanding its process transformation capabilities through the development of an Artificial Intelligence (AI) and Operational Excellence program aimed at improving decision-making, process performance, and automation across business and operational functions.

The AI & Process Improvement Lead serves as both a technical practitioner and strategic leader, combining process improvement methodologies with advanced analytics, machine learning, digital modeling, and automation technologies to drive measurable improvements in safety, quality, productivity, reliability, and cost performance.

This position leads high-impact initiatives from concept through implementation, utilizing data-driven problem solving, experimentation, and cross-functional collaboration to develop scalable solutions. The role supports enterprise adoption of advanced analytics and AI-enabled decision-making while promoting continuous improvement practices throughout the organization.

• Lead improvement initiatives using a structured process-improvement framework combined with advanced analytics and artificial intelligence technologies.
• Partner with operations, engineering, business, and continuous improvement teams to standardize and scale successful solutions across multiple sites and business functions.
• Support development of data-driven operational systems through predictive analytics, process simulation, optimization techniques, advanced process control, and automated decision support.
• Apply statistical analysis, machine learning, forecasting, anomaly detection, and optimization methods to identify trends, uncover performance opportunities, and support informed decision-making.
• Leverage generative AI and large language models to improve knowledge management, enhance data accessibility, and streamline workflows.
• Identify opportunities to automate repetitive or transactional processes to improve efficiency, accuracy, and resource utilization.
• Manage projects from initiation through sustainment, including scope development, stakeholder engagement, timeline management, and benefit tracking.
• Complete advanced training in process improvement and AI methodologies; provide mentoring and coaching to engineers, analysts, and operational personnel as needed.
• Promote disciplined experimentation, statistical process control, and hypothesis-driven analysis.
• Develop an understanding of data governance, model lifecycle management, deployment practices, monitoring, and model maintenance to ensure reliable solutions.
• Maintain appropriate documentation, governance practices, and change-management controls.
• Lead organizational change efforts that encourage adoption of new technologies and processes.
• Create and maintain metrics, dashboards, standard work, and implementation guides that support repeatable success.
• Demonstrate strong commitment to ethical conduct, safety, quality, operational excellence, and business performance.
• Ensure solutions comply with applicable regulations, organizational policies, cybersecurity standards, and data protection requirements.
• Travel periodically to operational and corporate locations to support assessments, implementation activities, training, and knowledge transfer.
  
  

• Bachelor's degree in Engineering, Computer Science, Data Science, Mathematics, or a related STEM discipline, or an equivalent combination of education and relevant experience.
• Several years of experience in process improvement, manufacturing operations, analytics, automation, engineering, or related technical fields.
• Strong interest in applying artificial intelligence and advanced analytics to solve business and operational challenges.
• Ability to learn and utilize technical systems, operational technologies, and large datasets.
• Foundational understanding of analytical model development, deployment, and maintenance, with a willingness to expand expertise in model lifecycle management practices.
• Strong verbal and written communication skills with the ability to translate technical concepts for non-technical audiences.
• Proven ability to lead cross-functional initiatives and drive results through influence, collaboration, and change management.
  
  

Ability to interpret complex information, design experiments, and evaluate data-driven solutions.

Knowledge of machine learning, artificial intelligence, statistical methods, data engineering, and operational systems.

Applies structured methodologies and quantitative analysis to identify root causes and develop effective solutions.

Successfully manages priorities, resources, risks, and project execution.

Facilitates adoption of new technologies and processes while overcoming organizational barriers.

Effectively presents ideas and findings to both technical and business stakeholders.

Promotes repeatability, accuracy, sustainability, and measurable business value.

Aligns improvement initiatives with organizational objectives and measurable outcomes.