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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Global Engineer, Industrial Data Science - **Company:** Nexteer Automotive - **Location:** Saginaw, MI, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Microsoft Excel, Artificial Intelligence, Business Analytics Applications, Data Analysis, Computer Vision, Automation of Tests, Microsoft Azure, Databases, Data Cleansing, Data Governance, Data Visualization, Relational Databases, Decision Support Systems, Supervisory Control and Data Acquisition (SCADA), Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Message Queuing Telemetry Transport (MQTT), NumPy, Operational Data Store, Reliability Engineering, Power BI, OPC Unified Architecture, SAP (Applications), Statistical Process Control (SPC), SQL Databases, Technical Data Management Systems, Visual Analytics, Digital Twin, Pandas, Matplotlib, Pyspark, Scikit Learn, Information Technology, Data Analytics, Plotly, Restful APIs, Data Pipelines, Qad, Databricks - **Published:** September 22, 2026 - **Apply:** https://jobs.nexteer.com/talentcommunity/apply/1432100600/?locale=en_US ## About the Role * 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., * 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., Python, SQL, data preparation, statistical analysis, regression, hypothesis testing, experimental design, time-series analytics, and machine learning. Python Analytics Pandas, NumPy, scikit-learn, Matplotlib, Seaborn, Plotly, or equivalent libraries. Business Intelligence Power BI, advanced Microsoft Excel, data modeling, dashboard development, and KPI standardization. Manufacturing Systems MES, manufacturing traceability, SCADA, historians, PLC data, OPC UA, MQTT, SAP, QAD, or comparable platforms., * Bachelor's degree in Data Science, Industrial Engineering, Manufacturing Engineering, Computer Science, Statistics, Applied Mathematics, Mechanical Engineering, Electrical Engineering, or a related technical field. * Master's degree in Data Science, Analytics, Engineering, or a related field is preferred. ## Description 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 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. Collaboration and Leadership * 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, Azure analytics services, Databricks, PySpark, predictive maintenance, anomaly detection, computer vision, digital twins, simulation, and industrial AI. ## Related Videos - [Vectorize all the things! 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