SKF works to reduce friction, make things run faster, longer, cleaner and safer. Doing this in the most effective, productive and sustainable way has made the SKF Group a leading global supplier of products, solutions and services within rolling bearings, seals, mechatronics, services and lubrication systems. Services include technical support, maintenance services, condition monitoring, asset efficiency optimization, engineering consultancy and training.
SKF is now looking for a results-oriented
Business Process Automation & AI
to join our growing team.
You will be part of the Future Factory team tasked to transform and equip the SKF Group Manufacturing footprint with digital solutions, skills and knowledge to strengthen World Class Manufacturing for competitive advantage. You will work in creating business value in manufacturing process and engineering through bridging digital and physical with one digital platform of information to accelerate data reuse and accuracy for data driven efficiency improvement of production.
Your day-to-day work and responsibilities
- Enable the exploration of new capabilities or performance optimization in a digital factory environment, by means of digital twins, AI, and machine learning
- Design the framework of data, connections, models, and software standards that will enable the creation of a digital twin or digital copy of SKF manufacturing process, manufacturing facilities, or manufacturing system.
- Design and improve use of data, analytics, AI and machine learning around manufacturing systems to drive increases in productivity, product quality, and business feedback, to help plan, direct, and coordinate manufacturing processes within SKF Manufacturing community in a data driven manner.
- Work with staff throughout SKF organization to refine a product's design, plan and develop new manufacturing systems, and support other roles in their pursuit of actionable data in the manufacturing environment
We expect you to have
- Excellent interpersonal communication and organizational skills to contribute as a leading member of global, distributed transformation team focused on delivering quality services and solutions for the global SKF Manufacturing footprint
- MSc in Mechanical Engineering, Applied Physics, Computer Science, or similar
- Experience in manufacturing business processes
- Experience in manufacturing systems (e.g. SIEMENS TeamCenter, ..), engineering systems (e.g. PTC Windchill, ..), or similar
- Experience in digital twins (e.g. process planning, process simulation, logistics and throughput simulation, or cost simulation, ..), software robotics (e.g. robotic process automation, …), or artificial intelligence / machine learning
- Experience in working with manufacturing master data, data structure, data classification, and data analytics
- Fluent in English, both verbal and in writing
- Opportunity to work with diverse workforce in an international environment
- Interesting challenges in driving the digital readiness and digital solutions development to the full scope of SKF Manufacturing footprint
- Professional growth opportunities in working with some of the absolute latest technologies*
*) SKF is featured in e.g. Microsoft Ignite 2021, Microsoft Build 2021, Microsoft Hybrid Cloud Digital Event 2021 for their Hybrid Cloud and Industrial IoT technology deployments
Preferred location: Gothenburg (GOT) / Schweinfurt (GER) / Airasca (AIR) or any other major SKF manufacturing site.
How to apply
If you are interested and meet the above requirements, please submit your application with CV and cover letter (in English) no later than July 9, 2021. Please note that we can't accept applications via e-mail.
SKF’s mission is to be the undisputed leader in the bearing business. SKF offers solutions around the rotating shaft, including bearings, seals, lubrication management, condition monitoring and maintenance services. SKF is represented in more than 130 countries and has around 17,000 distributor locations worldwide. Annual sales in SEK is around 75 billion and the number of employees ca 41,000.