The Engineering Operational Excellence (OpEx) Lead drives engineering transformation across global New Product Introduction (NPI) and engineering value streams. This role focuses on improving speed, quality, and productivity through data-driven and digital solutions.
This position is ideal for a candidate with 5–10 years of experience who is highly analytical, comfortable with data and modern AI tools, and eager to create measurable impact. The role involves collaboration with global engineering, program management, test labs, and NPI teams while promoting a culture of operational excellence aligned with the Garrett Excellence Model (GEM).
Key Responsibilities
1) Data Analytics and Process Intelligence
- Build and maintain dashboards, KPI trackers, and performance models using Excel, Power BI, or similar tools.
- Perform statistical analysis to identify inefficiencies and opportunities for improvement.
- Use AI tools to accelerate data analysis and insight generation.
- Develop predictive models for engineering cycle time and risk analysis.
- Present data insights clearly to stakeholders.
2) AI-Driven Digitalization and Automation
- Promote use of AI tools across engineering processes.
- Identify and implement automation for repetitive tasks and reporting.
- Support integration of digital tools into existing engineering workflows.
- Stay updated on emerging AI technologies and recommend applications.
3) Process Improvement and Lean Execution
- Support transformation of engineering and NPI processes.
- Participate in value stream mapping, Kaizen events, and problem-solving activities.
- Apply Lean and Six Sigma concepts with support from senior leaders.
- Maintain process documentation and governance practices.
4) Reporting and Results Delivery
- Track improvements in engineering productivity, cycle time, and quality.
- Support planning cycles such as AOP and forecasting with performance data.
Education and Qualifications
Required:
- Bachelor’s degree in Statistics, Data Science, Mathematics, Computer Science, Software Engineering, or a related field
Preferred:
- Master’s degree in Data Science, Analytics, Engineering Management, or Industrial Engineering.
- Certification or coursework in AI, machine learning, or analytics.
Experience Required
- 5–10 years of relevant experience.
- Strong experience in data analysis and problem-solving.
- Hands-on experience with Excel and data visualization tools like Power BI or Tableau.
- Exposure to AI tools or automation.
- Industry experience in Automotive or Manufacturing is a plus.
Key Skills
- Strong data and analytics skills
- Experience with AI tools and automation
- Statistical and quantitative thinking
- Problem-solving mindset
- Clear communication and data storytelling
- Learning agility and growth mindset
- Ability to collaborate with global teams
Preferred Certifications:
- Lean Six Sigma (Green Belt or higher)
- Agile/Scrum
- Data Science or AI certifications
Garrett is a cutting-edge technology leader delivering differentiated solutions for emission reduction and energy efficiency. We are passionate about innovating for mobility and beyond. With a nearly 70-year legacy, we serve customer worldwide with passenger vehicle, commercial vehicle, aftermarket replacement, and performance enhancement solutions.
Garrett Motion, a technology innovator and leader, has been serving customers globally for 70 years. Our mission is to deliver solutions that reduce emissions, enhance energy efficiency, and support zero-emission vehicles. We are passionate about innovating for mobility and beyond. Garrett’s technologies enable sustainable automotive and industry transformation.
Our portfolio includes turbochargers, electric turbos (E-Turbo) and electric compressors (E-Compressor) for both ICE and hybrid powertrains. In the zero-emissions vehicle category, the company offers fuel cell compressors for hydrogen fuel cell vehicles (FCEVs) as well as electric propulsion and thermal management systems for battery electric vehicles (BEVs).
We take pride in our extensive infrastructure, boasting five R&D centers, 13 manufacturing sites, and a team of 9,300 professionals spread across over 20 countries.
