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.
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 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 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.
- Strong experience developing optimization models for man