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About This Role
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 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
Required Qualifications
- 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.
Preferred Qualifications
- 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.
Technical Expertise
Area
Expertise
Industrial Data Science
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.
Manufacturing Engineering
APQP, launch support, process capability, SPC, root cause analysis, corrective action, Lean, and Six Sigma.
Advanced Technologies
Azure analytics services, Databricks, PySpark, predictive maintenance, anomaly detection, computer vision, digital twins, simulation, and industrial AI.
Education Requirements
- 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.
This position is not eligible for employer-sponsored work authorization. Do not apply for this role if you will need immigration sponsorship now or in the future. This includes direct company sponsorship, entry of Nexteer as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc.)
Nexteer is an Equal Opportunity Employer Disabled/Minorities/Veterans/Women
Nexteer is an E-Verify employer. E-Verify is an internet-based system operated by the Department of Homeland Security (DHS) in partnership with the Social Security Administration (SSA) that allows participating employees to electronically verify the employment eligibility of their newly hired employees in the United States.
Accessibility for Applicants with Disabilities: If you need a reasonable accommodation for the online application process due to a disability, please email [email protected]. Please provide your contact information and details about the posted position of interest. Only inquiries regarding assistance for those who need an accommodation with the online application process due to a disability will be returned.
About Nexteer
Nexteer Automotive is a global leader in advanced steering and motion control systems, enabling intuitive, safe, and comfortable mobility for drivers around the world. Our innovative product portfolio includes Electric Power Steering (EPS), Steer-by-Wire™, Driveline, and Advanced Driver Assistance Systems (ADAS) technologies that support electrification, software-defined vehicles, and autonomous driving.
With approximately 13,000 employees and operations in more than 20 manufacturing plants and multiple technical centers globally, Nexteer partners with nearly every major global and regional automotive manufacturer.
Employment Agencies
Employment agencies are an important component of our talent acquisition strategy, and we value the partnerships we have built with our preferred vendors. We will not accept unsolicited resumes from employment agencies for any employment opportunity. All resumes submitted by search firms to any employee at Nexteer Automotive or directly to hiring managers in any form without a signed Nexteer Automotive Agency Agreement on file, and search engagement for that position, will be deemed unsolicited in nature, and no fee will be paid in the event the candidate is hired as a result of the referral or through other means. Additionally, any resume submitted without a contract in place will be considered Nexteer Automotive property and no fees will be paid to the agency if the candidate is hired.
Please note, agency agreements will only be valid if in writing and signed by a member of Nexteer’s Human Resources team. No other Nexteer employee is authorized to bind Nexteer to any agreement regarding the placement of candidates by an agency.
Nearest Major Market: Detroit
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Nexteer Automotive, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Nexteer Automotive AI Hiring
Nexteer Automotive has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Auburn Hills, MI, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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