Data & AI Governance Lead
14 hours ago
Barcelona
ph3Data AI Governance Lead /h3pData 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. /ppBrings 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. /ppExperienced 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. /ppKnown 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. /ph3Core Experience Impact /h3ulliSupport 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). /liliData 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. /liliPartner with domain leads to establish scalable governance frameworks and maintain global data‑readiness assessments across critical business domains. /liliExternal 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. /liliContribute to master data management initiatives for Consumer Health, strengthening consistency, quality, ownership, and reuse of key commercial data assets. /liliAI 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. /liliDefine fit‑for‑purpose governance policies and processes that ensure data is generated with quality and consumed compliantly and confidently across the business. /liliEstablish 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. /liliCollaborate 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. /liliTranslate data and AI governance frameworks into practical system and workflow requirements, in partnership with Data AI product teams and IT. /liliBuilt strong partnerships with enterprise and local Stewards to capture domain insight, resolve adoption barriers, and continuously improve governance implementation. /liliServe as the interface to Legal, Privacy, Compliance, Regulatory, and Ethics teams to interpret requirements and design pragmatic, fit‑for‑purpose governance frameworks. /liliPartner with existing data use and global compliance councils to operationalize Responsible AI standards (fairness, transparency, privacy, safety, explainability, human oversight) across the lifecycle. /liliCombine strategic leadership with hands‑on contribution when needed (e.g., acting as data steward, process owner, or project manager for governance initiatives). /liliDrive 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. /li /ulh3Qualifications Competencies /h3ulliMaster’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. /liliMinimum 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. /liliSenior experience in Consumer Health, Pharma, FMCG, or other regulated commercial environments, with strong understanding of commercial, market, customer, product, and external data domains. /liliDemonstrated leadership capability in cross‑functional projects, with experience establishing and embedding MDM governance structures across business, data, and technology teams. /liliStrong 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. /liliPractical 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. /liliProven 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. /liliStrong 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. /liliDeep 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. /liliStrong stakeholder leadership and influencing skills, able to bridge business, commercial, product, data, technology, legal, privacy, compliance, and ethics communities without relying on formal authority. /liliStrategic 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. /liliExcellent 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. /liliFluent in English must other language beneficial. /li /ulh3Location /h3pSpain: Cataluña: BarcelonabrPortugal: Estremadura: Carnaxide /ph3Reference Code /h3 h3Equal Employment Opportunity /h3pBayer welcomes applications from all individuals, regardless of race, national origin, gender, age, physical characteristics, social origin, disability, union membership, religion, family status, pregnancy, sexual orientation, gender identity, gender expression or any unlawful criterion under applicable law. We are committed to treating all applicants fairly and avoiding discrimination. /p /p #J-18808-Ljbffr