Global Engineer, Industrial Data Science

Nexteer Automotive Corporation•Saginaw, MI

About The Position

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.

Requirements

Minimum 5+ years of relevant experience in manufacturing analytics, quality analytics, industrial engineering, manufacturing engineering, automation, data science, or a related field.
Demonstrated experience applying data analysis to manufacturing, quality, launch, or operational improvement problems.
Practical proficiency with Python for data preparation, analysis, visualization, automation, and model development.
Working proficiency with SQL and relational data concepts.
Experience developing business intelligence solutions using Power BI and advanced Microsoft Excel.
Fluent English and the ability to explain complex analytics concepts to non-technical audiences.
Strong analytical thinking, structured problem-solving, written communication, visualization, and presentation skills.
Ability to work independently and within global, cross-functional teams, create accountability, and lead by example.
Ability to travel locally and internationally.

Nice To Haves

Automotive, discrete manufacturing, or high-volume manufacturing experience.
Project leadership and global cross-functional collaboration experience.
Experience with Azure analytics services, Databricks, PySpark, predictive maintenance, anomaly detection, computer vision, digital twins, simulation, or industrial AI.

Responsibilities

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 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.
Support APQP, product and process launches, root cause analysis, corrective actions, and process capability improvement.
Apply Lean, Six Sigma, DMAIC, A3, SPC, and structured problem-solving methods supported by objective data analysis.
Quantify and validate operational and financial benefits from analytics-driven improvements.
Support replication of successful solutions across plants, products, and regions.
Lead or support cross-functional analytics projects involving global and regional manufacturing teams.
Coach engineers and plant personnel in data literacy, statistical thinking, visualization, and data-driven problem solving.
Share standards, best practices, and lessons learned across the global manufacturing organization.
Maintain awareness of industrial analytics practices and recommend improvements to Nexteer methods and standards.