Process Data Analytics Engineer
Corning ·
- Category
- Data analytics
- Experience
- 3+ years
Corning ·
2025028d-7acd-4c20-b3da-c49a0d760dac:19000101_000001 · Fort Worth, TX, US
gilead · United States - California - El Segundo
Experity · Bengaluru Office
theapexgroup · New York, East 52nd Street
Data analytics engineer embedded with manufacturing/process engineers at Corning's Optical Communications segment in Shanghai, analyzing MES and quality/measurement data with SQL, Python, and statistics, building dashboards, and contributing to ML/AI and computer vision analytics across APAC sites.
Requisition Number: 78182
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How do we do this? With our people. They break through limitations and expectations – not once in a career, but every day. They help move our company, and the world, forward.
At Corning, there are endless possibilities for making an impact. You can help connect the unconnected, drive the future of automobiles, transform at-home entertainment, and ensure the delivery of lifesaving medicines. And so much more.
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Our Optical Communications segment has recently evolved from being a manufacturer of optical fiber and cable, hardware and equipment to being a comprehensive provider of industry-leading optical solutions across the broader communications industry.This segment is classified into two main product groupings – carrier network and enterprise network. The carrier network product group consists primarily of products and solutions for optical-based communications infrastructure for services such as video, data and voice communications. The enterprise network product group consists primarily of optical-based communication networks sold to businesses, governments and individuals for their own use.
• Perform statistical analysis and interpret results to support manufacturing and engineering process improvement.
• Partner closely with process engineers as an embedded analytics resource to investigate process behavior, document performance, and identify opportunities to reduce variability and improve control.
• Prepare, clean, and organize data for analysis, including basic transformation and structuring of data from multiple sources.
• Analyze existing manufacturing and quality datasets, with emphasis on MES data and part quality data from measurement systems.
• Apply appropriate statistical methods to solve process and quality problems and to support hypothesis-driven investigations.
• Develop dashboards, reports, and visualizations to communicate process performance and analytical findings to stakeholders.
• Contribute to advance analytics solutions in machine learning, AI, and computer vision
• Support APAC teams in leveraging data more effectively to monitor processes, identify trends, and enable data-driven decision-making.
• Contribute to the development of standardized reporting and analytics approaches across sites and teams where appropriate.
• Collaborate with cross-functional partners in engineering, manufacturing, quality, and other functions to understand needs and deliver practical analytical solutions.
• Train and coach stakeholders on data usage, reporting tools, and interpretation of analytics results to build organizational capability.
• Deliver value in environments where problems may be loosely defined and data may be incomplete, requiring initiative, sound judgment, and attention to detail.
• Bachelor’s degree in Statistics, Data Science, Mathematics, Computer Science, Industrial Engineering, or a related technical discipline.
• Approximately 3+ years of relevant experience in data analytics, statistical analysis, manufacturing analytics, process engineering analytics, or a related field.
• Strong knowledge of statistics, data analysis, and analytical problem solving.
• Strong SQL skills for data extraction, transformation, and analysis.
• Strong Python skills for data analysis, visualization, and related workflows.
• Experience creating data visualizations, dashboards, and reports to support decision making.
• Ability to work effectively with manufacturing and quality data.
• Ability to prepare, clean, and structure data for analysis.
• Basic knowledge of data engineering concepts and data workflows.
• Strong written and verbal communication skills in English.
• Ability to explain analytical findings clearly to technical and non-technical stakeholders.
• High attention to detail and strong organizational skills.
• Direct manufacturing experience strongly preferred.
• Experience with Databricks strongly preferred.
• Experience with Power BI for dashboarding and reporting.
• Experience with JMP for statistical analysis and visualization.
• Familiarity with machine learning methods and their practical application in business or manufacturing settings.
• Familiarity with LLMs, AI tools, or emerging analytics technologies.
• Understanding of statistical process control (SPC), variation reduction, and manufacturing performance metrics.
• Experience integrating or analyzing data from MES, quality systems, and measurement equipment.
• Understanding of manufacturing processes and industrial data systems.
• Strong collaborator who can work closely with process engineers and cross functional teams to solve problems.
• Self-motivated and able to work independently with limited direction.
• Comfortable operating in ambiguous environments and defining a path forward.
• Able to train, coach, and influence others in the use of analytics and data-driven thinking.
• Strong communication and presentation skills, including the ability to work effectively across countries, teams, and cultures within APAC.
• Detail oriented, with a strong focus on accuracy, data integrity, and follow-through.
Corning is committed to providing equal employment opportunities and considers requests for reasonable accommodations in accordance with applicable laws. Individuals with disabilities or sincerely held religious beliefs may request reasonable accommodations to participate in the application or interview process, perform essential job functions, or access other benefits and privileges of employment. To submit a request for reasonable accommodation related to disability or religion, please contact us at [email protected].
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