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.

- Communicate complex analytical findings to both technical and non-technical stakeholders.

Required Qualifications

- 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 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 added advantage.

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

Ideal Candidate A highly collaborative and business-oriented data scientist who combines deep manufacturing expertise, strong industrial data analytics skills, and practical experience building energy optimization solutions. The ideal candidate can effectively bridge the gap between operations, engineering, and data science teams to deliver scalable AI solutions that improve energy performance and support Kimberly-Clark's sustainability objectives.

Pay: ₹1,000,000.31 - ₹3,500,000.94 per year

Work Location: In person

📌 Data Scientist (Hyderabad)
🏢 Nyb infotech
📍 Hyderabad