Working Student Predictive Maintenance / ARAC Monitoring (m/f/d)

We electrify the future - with sustainable technology and genuine teamwork

thyssenkrupp Electrical Steel is part of thyssenkrupp Steel, one of the world's leading manufacturers of high quality flat steel. Our specialty: corn-oriented electric band - a technically highly complex material that ensures that current flows efficiently and reliably.

What makes our product so special? It is tailor-made for the requirements of modern energy supply. Production requires precision, know-how and the most modern technologies - and this is what makes our work so exciting. Whether wind power plants or the power grids of tomorrow - our electric band is used wherever energy needs to be transported safely and low-loss. In Gelsenkirchen and Isbergues (France), we produce materials that help globally replace fossil energy with renewable energy.

With innovative processes, highest quality standards and a strong team, we make a direct contribution to decarbonization and shape the energy infrastructure of tomorrow.

You want to be part of this future?

Then come to us - and work with a world that becomes greener, smarter and electric.

Working closely with our process experts to develop, configure and optimize ARAC monitoring.

Evaluation and use of iba recordings as a basis for monitoring logics and data analysis.

Analysis of machine and process data for identifying deviations and patterns.

Support in the documentation, structuring and quality assurance of monitoring models.

Processing of data for specialist departments and review terminals.

Processing into methods and tools of predictive maintenance.

You are a student of electrical engineering, mechatronics, automation technology, technical computer science or a comparable technical degree program.

In addition, you have basic knowledge of databases (e.g. SQL basics, table structures) and are safe to use MS Office, especially Excel.

You have a basic technical understanding of industrial equipment, sensor technology or measurement variables.

First contact points with iba systems, in particular with ibaPDA, ibaAnalyzer or similar tools, are a plus.

Basic knowledge of the analysis and interpretation of time series data would be advantageous.

You are interested in predictive-maintenance approaches and digital monitoring concepts.

First experiences in data analysis (e.g. Python or other analysis tools) are helpful but not necessary.

Structured, analytical and reliable working methods are your strengths.

High readiness and technical interest will bring you with you.

Team skills and communication strength in exchange with technical experts complete your profile.

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