Linde is looking to convert large amounts of data into actionable insights in the LGUS Equipment Health Center (EHC) to continuously improve advanced diagnostics and health analytical systems, and to monitor and improve the reliability, efficiency, maintenance, total lifetime cost, and safety of plant equipment.
Requirements
- Experience with Microsoft tools and SharePoint development.
- Knowledge of predictive modeling, test design, and database querying.
- Experience with VBA code or equivalent and SQL (MS/T-SQL) including stored procedures, indexes, jobs, etc.
- Familiarity with data visualization tools (e.g. Tableau, D3, SSRS, Power BI etc.).
- Familiarity with scripting and data analysis programming languages (e.g. Python, R, etc.).
- Familiarity with plant-wide industrial Historian (e.g. GE Proficy).
- Strong analytical and quantitative skills.
Responsibilities
- Lead, develop and manage Equipment Health Center KPIs.
- Analyze operational data from multiple sources and transform them into information by leveraging a variety of statistical techniques such as segmentation, regression, survival analysis, principal component analysis, scenario and sensitivity analysis, neural networks, and machine learning.
- Create visualizations and/or interactive dashboards for displaying results to the EHC rotating and electrical experts using open-source and commercially available visualization and dashboard tools.
- Work with discipline experts to develop and build predictive models & new predictive technologies.
- Cooperate closely with CoE software and tool developers on the further development of existing predictive technologies and the development of new ones.
- Work with regional reliability teams to set implementation goals of predictive technology tools, assist with tool implementation and track the progress.
- Research and prioritize AI use cases.
Other
- Strong in interdisciplinary teamwork.
- Potential to act as a Change Manager and catalyst for changes.
- Demonstrated ability to challenge status quo and improve existing processes/programs.
- Willingness to travel to production sites as needed.
- Bachelor’s or master’s degree in computer science or engineering and 2+ years of relevant work experience in a related field.
