This role requires a robust 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.
Role & responsibilities
Collaborate with clients internal Data Science team to scale and deploy Energy Optimization AI solutions across multiple mills.
Analyse manufacturing processes, energy consumption patterns, and operational constraints to identify optimization prospects
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 modelling 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.
Communicate complex analytical findings to both technical and non-technical stakeholders.
Preferred candidate profile
Bachelor's or Master's degree in Data Science, Engineering, Statistics, Applied Mathematics, or a related field
10+ years of experience in Data Science, Advanced Analytics, or AI within manufacturing environments.
Proven experience working with PI Historian (OSIsoft/AVEVA PI System) and industrial time-series data.
Robust experience developing optimization models for man