Senior Data Scientist – Manufacturing Sustainability & Energy Optimization

NYB Infotech; Hyderabad; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

NYB Infotech in Hyderabad, India invites a Senior Data Scientist to partner with internal DS and Manufacturing teams to scale an Energy Optimization AI solution across multiple mills. The role requires deep manufacturing analytics, industrial time-series data expertise, and the ability to translate business challenges into scalable AI and optimization solutions.

The ideal candidate will work with process engineers, energy managers, and data scientists to drive adoption of AI-powered

Qualifications

• Bachelor's or Master's in a quantitative field with 10+ years in DS/analytics in manufacturing.
• Experience translating business problems into scalable AI and optimization solutions.
• Proven ability to work with engineers and leadership to drive adoption.
• Strong knowledge of energy management and sustainability use cases.

Responsibilities

• Scale and deploy energy optimization AI across multiple mills.
• Analyze energy consumption, processes, and constraints to identify opportunities.
• Develop predictive and prescriptive models for energy efficiency.
• Leverage PI Historian and other data sources for insights.
• Collaborate with stakeholders to drive value realization.

Skills

Manufacturing analytics

Industrial data

Energy optimization

Business problem translation

Stakeholder communication

Education

Bachelor's or Master's in Data Science, Engineering, Statistics or related field

Tools

PI Historian (OSIsoft/AVEVA PI System)

Python

SQL

Databricks

Azure

Job description

NYB infotech is seeking an experienced Senior Data Scientist to partner with its internal Data Science and Manufacturing teams to scale an existing Energy Optimization AI solution across multiple manufacturing mills . The ideal candidate will bring deep expertise in manufacturing analytics, industrial data, and sustainability, with the ability to translate business challenges into scalable AI and optimization solutions that deliver measurable operational, financial, and sustainability outcomes.

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.

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.
• 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 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.