HOW YOU CAN MAKE AN IMPACT
- Within the program, you ensure target oriented development of predictive maintenance use cases and their implementation on existing and new fleets including Hardware/Software refits where needed
- Program Commercial Management: you make sure the various activities of the program are on track and aligned with overall program budget and regularly report about the progress to the Management team
- You keep track of relevant KPIs related to the program
- You are the first point of contact to the program and interact with various stakeholders (SRS Digital Solutions, SRS Division Management, Maintenance Locations, Manufacturing Divisions, Customers)
- You are an internal advocate for the cause of predictive maintenance solutions and persuade stakeholders about the benefits and value they can bring
- You help develop data-driven maintenance solutions to improve maintenance cost and to build customer-facing digital services as well as Stadler's overall condition monitoring strategy
HOW YOU WILL CONTRIBUTE
Required Skills:- You have proven experience in complex/large project or program management for new technologies & new processes implementation
- Strong analytical and problem solving skills: A system engineering mindset with the ability to understand projects on a technical level and highlight the value of the program's outcomes
- Excellent communication and interpersonal skills, with the ability to collaborate effectively with diverse teams
- Experience in stakeholder management and management reporting
- A strategic mindset to drive the program development toward a sustainable and profitable business
- A very good command of German and English
Preferred Skills:
- Experience in the railway industry, particularly in roiling stock maintenance
- Experience in Condition-Based / Data-driven Maintenance with rolling stock or in other industries
- Experience working with AVIS (automated inspection vehicle systems) or similar
- Experience in various project management methodologies, including AGILE
- Experience with popular data science and machine learning processes