ZT Systems is looking to define and execute a roadmap for applying artificial intelligence and machine learning in manufacturing, aiming to identify high-impact use cases, prepare the organization for adoption, and drive successful implementation of AI/ML applications to improve manufacturing risk analysis, quality, and continuous improvement.
Requirements
- Demonstrated expertise in statistical and analytical methods such as regression analysis, correlation analysis, DOE, SPC, PFMEA, Gauge R&R, and commonality studies.
- Fluency with data-driven tools such as Minitab, JMP, Python, R, SQL (or equivalent) to analyze and interpret large, complex datasets.
- Experience with applying AI/ML toolsets to statistical problem solving, predictive analytics, or anomaly detection
- Strong background in leveraging manufacturing data (metrology, vision systems, event logs, quality data) to build AI/ML-enabled solutions.
- Advanced skills in mathematical computing with at least one programming language (e.g. Python, R, Java, or equivalents), and the ability to learn technical methods and tools independently.
- Advanced skills in data visualization / presentation skills, including the ability to simplify results & statistical concepts into simple and actionable insights.
- Ability to convert complex (often data driven) topics to clear overviews and insights.
Responsibilities
- Define and implement new systems, processes, or frameworks that support the smart factory vision, including automation, metrology, advanced inspection, and predictive analytics.
- Define the organizational, data, and process changes required to prepare the business for AI/ML integration.
- Drive the design, development, and deployment of AI/ML solutions, ensuring successful adoption across factories.
- Apply AI/ML techniques to analyze manufacturing data sets – including metrology, vision inspection, event data, test results – conduct regression analysis, correlation studies, and commonality analysis.
- Leverage deep, data-rich environments and tools (e.g., Minitab, JMP, Python, R, SQL) to generate insights that improve yield, reliability, and throughput.
- Apply advanced statistical and analytical methods (regression, correlation, DOE, SPC, PFMEA, Gauge R&R, commonality studies) to identify, quantify, and control risk in complex manufacturing environments.
- Use predictive analytics to inform PFMEA analyses that will result in actionable process controls, ensuring proactive prevention of variation rather than reactive correction.
Other
- Lead or contribute to transformation initiatives, helping set new standards for how ZT approaches manufacturing risk analysis, quality, and continuous improvement.
- Partner with leadership to define the vision and strategy for AI/ML adoption across manufacturing operations.
- Work with factory engineering, quality, and operations to identify, evaluate, and prioritize AI/ML use cases that deliver measurable business value.
- Collaborate across design, quality, manufacturing, test, and supplier engineering to drive solutions that integrate seamlessly into production.
- Champion the cultural and operational transformation required for AI/ML success, including training and upskilling the industrial engineering team in new methods and approaches for mathematical computing.
