The ideal candidate will possess a strong background in Six Sigma methodologies and a working understanding of artificial intelligence (AI) applications focusing on enhancing the reliability and efficiency of our physical asset management systems and processes through analytical techniques to predict and prevent failures.
Key Responsibilities:
- Create and execute reliability strategies and plans to enhance system performance and minimize downtime.
- Utilize Six Sigma methodologies to identify and eliminate process inefficiencies.
- Apply AI techniques to analyze data and predict potential failures.
- Conduct root cause analysis and implement corrective actions to prevent recurrence of issues.
- Collaborate with crossfunctional teams to drive continuous improvement initiatives.
- Maintain and update reliability databases and documentation.
- Provide training and support to team members on reliability best practices and tools.
Qualifications:
- A Bachelors degree in an engineering field with at least 4 years of relevant experience in reliability engineering.
- Certified Six Sigma Green Belt or equivalent and equivalent certification in reliability engineering.
- Proficiency in AI and machine learning tools and techniques.
- Strong analytical and problemsolving skills.
- Excellent communication and teamwork abilities.
- Experience with reliability modeling tools and methodologies.
- Familiarity with industry standards and best practices in reliability engineering.
- Preferred Skills:
- Experience in the oil and gas industry.
- Knowledge of predictive maintenance and condition monitoring techniques.
- Proficiency in data analysis software such as Python R or MATLAB.
- Strong project management skills.
- Working Conditions:
- This position may require occasional travel to various sites.
- Ability to work in a fastpaced and dynamic environment.