Global Engineer, Industrial Data Science

Global Engineer, Industrial Data Science- Auburn Hills

Location

Auburn Hills, MI, US

Division:

US-Corporate (COR)

Req ID:

55685

Position: Global Engineer, Industrial Data Science

Position: Global Engineer, Industrial Data Science

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!

About the Role

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.

Key Responsibilities

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

• Manufacturing Analytics and Data Science

• 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.

• Manufacturing Data Integration

• 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.

• Digital Manufacturing and Industry 4.0

• 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.

• Visualization and Decision Support

• Develop automated dashboards, data models, and visual analytics using Power BI, Python, and approved enterprise platforms.

• Establish clear definitions and governance for global manufacturing KPIs.

• Translate complex analytical findings into practical recommendations for plant teams, engineers, and leadership.

• Create concise technical documentation, model summaries, business cases, and training materials.

• Quality, Launch, and Continuous Improvement

• Support APQP, product and process launches, root cause analysis, corrective actions, and process capability