Senior Data Scientist - Manufacturing Sustainability & Energy Optimization

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.