About the Role
As a member of our global team for enterprise data & advanced analytics, you lead a team that develops methodologies and tools for data-driven decision making leveraging advanced machine-learning concepts. You guide the business in application and implementation of these new solutions for insight generation, support internal competence build-up and roll-out, and drive collaboration within a partner ecosystem across industry and academia.
Passionate about the environment and climate change? Ready to be part of the future of the energy transition? The Siemens Energy Data Analytics & AI team plays a significant role in driving the energy transformation.
Your Profile
- University degree in Computer Science, Mathematics, Physics, Engineering, or related field
- Exceptional leadership skills and at least 2+ years of experience as a team lead, ideally in an industry environment
- Excellent analytical skills, strategic thinking, and a proven track record of developing and managing complex ML and AI applications
- Profound knowledge in data science, statistical modelling, programming (Python, R, C/C++, etc.), and computing techniques
- Strong customer focus and excellent interpersonal skills, open-minded and willing to learn
- Ability to navigate easily in a multidisciplinary, multinational, global team including external partners
- Fluent in written and spoken English, excellent communication and presentation skills
- Enthusiastic about data and analytics and driving the use of data across all parts of a business and ecosystem
Your Responsibilities
- Build up and lead a team of experts for the development of novel machine learning concepts for data-driven decision making along the value chain
- Closely work with various teams and stakeholders across the business coordinating the development, application, and implementation of new solutions for insight generation
- Ensure high quality of the developed models and tools by defining standards, introducing industry accepted tools and methodologies
- Support the internal competence build-up and roll-out of data science through technical training programs, know-how sharing, and regular communication
- Actively follow and evaluate new technologies/methods in the field of machine learning to leverage new solutions and understand latest trends
- Collaborate with universities, research institutes, and partners from industry and engage in thought leadership
- Present methodologies, procedures, and project results to senior stakeholders across different functions