Overview In this role you will advance Lubrizol's Surface Science portfolio by enabling safe, scalable commercialization of natural and bio-based polymers. You will collaborate with R&D, Operations, Supply Chain, and Commercial teams to deliver robust manufacturing solutions. You will lead scale-up activities, apply data-driven methods to improve processes, and identify opportunities for safety, productivity, and quality gains. This position offers a chance to shape manufacturing capability and drive sustainable growth within a global, inclusive environment.
Compensation / Benefits- Competitive salary with performance-based bonus plans
- 401(k) match + Age-Weighted Defined Contribution
- Comprehensive medical, dental & vision coverage
- Health Savings Account (HSA)
- Paid holidays, vacation, and parental leave
- Flexible work environment
Responsibilities- Lead and support new product scale-ups through structured experimentation, pilot trials, and manufacturing authorization activities
- Identify, benchmark, and deploy Most Effective Technologies (MET) to strengthen manufacturing capability and operational excellence
- Apply data-driven approaches (DOE, statistical analysis) to evaluate process performance and drive continuous improvement
- Identify and implement opportunities to improve process safety, capability, productivity, quality, and cost
- Partner with commercial and technical teams to align process technology strategies with business growth objectives
- Lead projects and present status updates, clearly communicating goals, progress, and results
Key requirements- 8+ years of experience in chemical, specialty materials, or polymer manufacturing environments
- Demonstrated capability in experimental design, data analysis, and structured problem-solving methodologies
- Proven ability to collaborate effectively with cross-functional and geographically distributed teams
- Strong written and verbal communication skills
- cross-functional collaboration
- clear communication
- problem-solving
- experimental design
- data analysis
- DOE (design of experiments)
