to partner with its internal Data Science and Manufacturing teams to scale an existing
Energy Optimization AI solution across
multiple manufacturing mills . The ideal candidate will bring deep expertise in manufacturing analytics, industrial data, and sustainability, with the ability to translate business challenges into scalable AI and optimization solutions that deliver measurable operational, financial, and sustainability outcomes.
This role requires a solid understanding of manufacturing processes, energy systems, industrial time-series data, and advanced analytics. The successful candidate will work closely with process engineers, mill leadership, energy managers, and internal data scientists to drive adoption of AI-powered optimization recommendations across Kimberly-Clark's manufacturing network.
Key Responsibilities
Collaborate with client’s internal Data Science team to scale and deploy Energy Optimization AI solutions across multiple mills.
Analyze manufacturing processes,
energy consumption patterns, and operational constraints to identify optimization opportunities.
Develop and enhance predictive, prescriptive, and optimization models focused on reducing energy consumption and improving operational efficiency.
Leverage
PI Historian (AVEVA PI System)
and other manufacturing data sources to generate actionable insights and recommendations.
Define modeling strategies based on business objectives, process understanding, and site-specific operating conditions.
Build analytical frameworks that can be replicated and adapted across different manufacturing facilities.
Partner with business stakeholders, process engineers, and operations teams to drive adoption and value realization.
Conduct root cause analysis and identify key operating parameters influencing energy performance.
Monitor model performance and continuously improve solution accuracy, scalability, and business impact.