Data & AI Governance Lead

Data & AI Governance Lead is experience defining governance strategy, data quality frameworks, and responsible AI controls across global commercial environments. Strong track record of translating complex regulatory, privacy, compliance, and technical requirements into practical governance capabilities that improve data trust, audit readiness, AI readiness, and business adoption. Brings a rare combination of strategic leadership and hands-on delivery: establishing decision rights, stewardship and custodian models, policies, standards, data quality processes, master data management practices, and governance workflows that enable compliant, confident, and scalable data use across business, analytics, product, and IT teams. Experienced in shaping pragmatic Data & AI governance for Data Governance, AI, Agentic AI, and GenAI use cases with needed management, human oversight, privacy, transparency, and risk management. Skilled at partnering with Legal, Privacy, Compliance, Regulatory, Ethics, product teams, and external data providers to embed governance into systems and ways of working. Known for making governance a business enabler rather than a control function only: driving adoption through stakeholder engagement, education, change management, and clear accountability while helping teams move faster with trusted, high-quality, and compliant data.

Your Tasks and Responsibilities

• Support and deliver the data and AI governance strategy and multi-year roadmap, aligned with Consumer Health priorities and Data & AI ambitions, to yield measurable outcomes (e.g., improved data quality, audit readiness, and trusted, compliant data use).
• Data & AI compliance in contracts & TPAs — embed AI compliance and risk controls into third-party agreements (TPAs), vendor contracts, and internal data and AI engagements — defining AI and acceptable-use clauses, data-lineage and provenance requirements, IP and usage rights, bias and transparency expectations, and assurance/acceptance criteria — so the business stays AI-compliant and free of avoidable risk with both external vendors and internal teams.
• Partner with domain leads to establish scalable governance frameworks and maintain global data-readiness assessments across critical business domains.
• External data & vendor standards — establish governance expectations for external data collaborations and vendor data, including standards, transfer criteria, quality acceptance, usage rights, and AI-related controls.
• Contribute to master data management initiatives for Consumer Health, strengthening consistency, quality, ownership, and reuse of key commercial data assets.
• AI bias & fairness frameworks and launch readiness — co-develop AI bias and fairness frameworks and clear launch-readiness criteria that define when a GenAI or Agentic use case (e.g. “Talk to Data”) is good enough to go live — setting fairness and accuracy thresholds, evaluation and benchmarking approaches, human-oversight requirements, and go/no-go gates, with continuous monitoring after launch.
• Define fit-for-purpose governance policies and processes that ensure data is generated with quality and consumed compliantly and confidently across the business.
• Establish data lifecycle governance controls — covering data creation, approval, change management, versioning, lineage, retention, archiving, and retirement — to ensure data remains traceable, audit-safe, compliant, and fit for trusted business and AI use.
• Collaborate with adjacent data disciplines (e.g., metadata descriptions, reference and master data, data modeling, cataloging, lineage) to support fitness-for-use, AI readiness, and auditability.
• Translate data and AI governance frameworks into practical system and workflow requirements, in partnership with Data & AI product teams and IT.
• Built strong partnerships with enterprise and local Stewards to capture domain insight, resolve adoption barriers, and continuously improve governance implementation.
• Serve as the interface to Legal, Privacy, Compliance, Regulatory, and Ethics teams to interpret requirements and design pragmatic, fit-for-purpose governance frameworks.
• Partner with existing data use and global compliance councils to operationalize Responsible AI standards (fairness, transparency, privacy, safety, explainability, human oversight) across the lifecycle.
• Combine strategic leadership with hands-on contribution when needed (e.g., acting as data steward, process owner, or project manager for governance initiatives).
• Drive adoption through change management, communications, and education, making governance practical for business, marketing, sales, and analytics teams while building accountability, data literacy, and risk-aware innovation.

Who you are:

• Master’s degree in business, Economics, Data Science, Information Management, Computer Science, or a related field; advanced degree or relevant certifications in data governance, privacy, risk, compliance, or AI governance are an advantage.
• Minimum 10 years of solid professional experience in master data management, data governance, or data-driven transformation programs, ideally within global, matrixed, or regulated business environments.
• Senior experience in Consumer Health, Pharma, FMCG, or other regulated commercial environments, with strong understanding of commercial, market, customer, product, and external data domains.
• Demonstrated leadership capability in cross-functional projects, with experience establishing and embedding MDM governance structures across business, data, and technology teams.
• Strong knowledge of SAP S/4HANA in a master data context, with familiarity across enterprise systems such as ERP, PIM platforms including, CRM, and related commercial data ecosystems.
• Practical know-how in data models, system integrations, and Golden Record logic, including the ability to translate business requirements into robust master data structures and integration principles.
• Proven ability to design, operationalize, and scale enterprise data governance frameworks, including data ownership, stewardship, data quality, metadata, lineage, master/reference data, cataloging, controls, and KPI-based governance performance management.
• Strong AI, GenAI, Agentic AI, and analytics fluency, with practical understanding of data readiness, model lifecycle governance, human oversight, transparency, explainability, monitoring, change control, and risk mitigation.
• Deep knowledge of data privacy, regulatory, compliance, security, and third-party data usage requirements, with the ability to translate legal and risk requirements into practical business and technology controls.
• Strong stakeholder leadership and influencing skills, able to bridge business, commercial, product, data, technology, legal, privacy, compliance, and ethics communities without relying on formal authority.
• Strategic and conceptual thinker with the ability to identify cross-functional patterns, simplify complexity, prioritize value, and balance business opportunity with regulatory, ethical, operational, and technology risks.
• Excellent executive communication, facilitation, and change management capabilities, with the ability to drive adoption, build data literacy, and make governance practical for business and technical audiences.
• Fluency in English is a must and knowledge of other language is beneficial

This position may be allocated across multiple countries (Poland, Czechia or Hungary), and elements of the recruitment process may vary to ensure compliance with local employment laws.

At Bayer, we are committed to transparency and equal pay for equal work or work of equal value. Due to differences in currencies and local compensation structures, detailed salary information will be shared with candidates during the recruitment process once they are identified as potential prospects. Final compensation is determined based on objective factors such as experience, qualifications, and scope of responsibility. This position is eligible for variable pay components, such as performance‑based bonuses, awarded in accordance with the applicable employee group, role scope, and compensation structure.

Benefits may vary depending on role and employment conditions.

Location:

Poland : Mazowieckie : Warszawa || Czech Republic : Prague : Prague || Hungary : Pest : Budapest  

Division:

Consumer Health 

Reference Code:

875568