Position Summary: 

As an AI Governance Expert, you will be responsible for establishing and maintaining a robust governance framework for the ethical, secure, and compliant use of AI technologies across the Digital Grid portfolio. You will work closely with cybersecurity, data science and product teams to ensure AI systems are trustworthy, transparent and aligned with Siemens Energy’s sustainability and digitalization goals.

A Snapshot of your Day

How You’ll Make an Impact (responsibilities of role)

·       AI Governance Framework: Develop and implement AI governance policies, procedures and controls aligned with global standards (e.g., EU AI Act, NIST AI RMF).

·       Risk & Compliance: Identify and mitigate risks associated with Data, AI models, including bias, explainability, data privacy and cybersecurity.

·       Model Lifecycle Oversight: Establish model documentation, validation and monitoring protocols across the AI lifecycle.

·       Cross-functional Collaboration: Work with data scientists, engineers and compliance teams to embed Data governance in AI development and deployment.

·       Audit & Reporting: Support internal and external audits related to AI systems and ensure compliance with regulatory and ethical standards.

·       Training & Awareness: Conduct training sessions on responsible AI practices and governance for internal stakeholders.

·       Tooling & Automation: Evaluate and implement AI governance tools and platforms to automate compliance and monitoring.

What You Bring 

·       Bachelor’s or master’s degree in computer science, Data Science, Cybersecurity, Business or a related field.

·       5 to 8 years of experience in GRC, cybersecurity or data governance, with at least 2 years in AI/ML governance or data management.

·       Strong understanding of AI/ML technologies, data privacy laws and risk frameworks (e.g., NIST AI RMF).

·       Familiarity with regulatory frameworks such as the EU AI Act, EU Data Act and EU CRA.

·       Excellent communication, stakeholder engagement and analytical skills.

·       Experience with AI model documentation (e.g., Model Cards, Datasheets for Datasets).

·       Knowledge of adversarial ML, secure AI development and MLOps.

·       Certifications such as ISO/IEC 8000, ISO/IEC 42001, or equivalent.