Global Engineer, Industrial Data Science- Auburn Hills

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Location:

Auburn Hills, MI, US

Division: US-Corporate (COR)

At Nexteer, our strength lies in the diversity of our team—each member contributing unique backgrounds, experiences, and aspirations. We believe this diversity fuels our innovation, broadens our perspectives, and propels our collective growth.

For over a century, we’ve been innovators in the automotive industry. Our vision is clear - we are a global leading motion control technology company accelerating mobility to be safe, green and exciting. Our unwavering commitment to Quality, Collaboration, Integrity, and Accountability guides us as we solve motion control challenges for our global customers. If you’re ready to join a dynamic team that drives change and makes a difference, Nexteer welcomes you!

Nexteer is looking for a Global Engineer, Industrial Data Science - Manufacturing Engineering to develop, deploy, and continuously improve industrial analytics solutions across our global manufacturing operations. This role combines manufacturing knowledge, statistics, Python, machine learning, and data visualization to convert production, quality, equipment, traceability, and operational data into practical improvements in safety, quality, delivery, cost, launch performance, and equipment effectiveness.

You will collaborate with Manufacturing Engineering, Quality, Operations, Automation, IT/OT, and Digital Manufacturing teams to establish scalable analytics methods, common data standards, and reusable solutions supporting process optimization, predictive maintenance, digital twins, MES, IIoT, and industrial AI.

As a Global Engineer, Industrial Data Science, you will be responsible to:

• Analyze manufacturing, quality, maintenance, process, and traceability data to identify trends, losses, constraints, and improvement opportunities.
• Develop descriptive, diagnostic, predictive, and prescriptive analytics supporting scrap reduction, first-pass yield, throughput, OEE, process capability, equipment reliability, and warranty improvement.
• Build and validate statistical and machine learning models for anomaly detection, defect prediction, predictive maintenance, process variation, and manufacturing optimization.
• Apply experimental design and statistical methods to validate root causes and measurable business impact.
• Develop reusable Python-based analytics workflows, data products, and automation scripts for manufacturing engineering applications.

• Acquire, clean, transform, and connect data from PLCs, SCADA, MES, traceability systems, historians, quality systems, ERP platforms, sensors, and engineering databases.
• Develop and maintain scalable data pipelines and structured datasets for analysis, visualization, and model deployment.
• Partner with Automation, Controls, IT, and OT teams to improve data availability, contextualization, governance, integrity, and cybersecurity compliance.
• Support industrial connectivity using OPC UA, MQTT, SQL, REST APIs, and related manufacturing communication methods.

• Support Smart Factory, MES, IIoT, Digital Twin, Virtual Commissioning, simulation, and industrial AI initiatives.
• Develop analytics and optimization models that improve manufacturing system design, launch readiness, material flow, process settings, and production performance.
• Evaluate emerging analytics and AI technologies, conduct practical pilots, and define scalable manufacturing use cases.
• Contribute to global technical roadmaps, standards, reference architectures, and deployment playbooks for industrial data science.

• Develop automated…