Process Data Analytics Engineer

Process Data Analytics Engineer

Key Responsibilities

• 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.

• Required Qualifications

• 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.

• Preferred 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.

• Soft Skills

• 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.