Role Overview :

This role requires a strong 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.

Responsibilities :

- Collaborate with the 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.

Requirements:

- 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 manufacturing operations, with a focus on energy efficiency and sustainability use cases.

- Demonstrated ability to understand business problems and translate them into effective analytical and modeling solutions.

- Experience working directly with manufacturing operations, process engineers, and business stakeholders.

Technical Skills:

- Machine Learning and Advanced Analytics, Statistical Modeling and Predictive Analytics, Optimization Techniques and Prescriptive Analytics.

- Python, SQL, Databricks and Azure.

- Industrial Data Analytics and Process Monitoring.

- Root Cause Analysis and Process Optimization.

Preferred Experience:

- Exposure to sustainability, decarbonization, and energy management initiatives will be an added advantage.

- Experience working within cross-functional business, engineering, and analytics teams.