Associate Director - Data & AI Governance

About Carrier

Carrier, global leader in intelligent climate and energy solutions, is committed to creating innovations that bring comfort, safety and sustainability to life. Through cutting-edge advancements in climate solutions such as temperature control, air quality and transportation, we improve lives, empower critical industries and ensure the safe transport of food, life-saving medicines and more. Since inventing modern air conditioning in 1902, we lead with purpose: enhancing the lives we live and the world we share. We continue to lead because of our world-class, inclusive workforce that puts the customer at the center of everything we do. For more information, visit corporate.carrier.com or follow Carrier on social media at @Carrier.

About this role

We are seeking a strategic and visionary Data & AI Governance lead for defining, establishing, and enforcing an enterprise-wide governance framework for data, analytics, and Artificial Intelligence (AI) assets and practices. This leader will ensure that the organization's use of data and AI is ethical, compliant, secure, high-quality, and strategically aligned to drive business value across all global operations. This role requires to develop and enforce comprehensive AI and GenAI governance frameworks, covering ethical AI, data privacy, model risk, and regulatory compliance & also collaborate with legal, risk, and data teams to ensure responsible and transparent AI practices.  

We value our people and offer an extensive benefits package, with financial rewards including health insurance, retirement savings plan, and also lifestyle support with flexible working and parental leave. Plus, we’ll support your growth with paid-for external training programs and courses.

Key Responsibilities

Strategy & Framework Development

• Establish and operationalize a comprehensive, federated Data and AI Governance framework (policies, standards, operating model, decision rights) that aligns with global business strategy and emerging AI/ML initiatives.
• Define the vision and roadmap for enterprise data quality, master data management (MDM), data lineage, and metadata management programs, ensuring they support AI readiness.
• Secure executive sponsorship and budget for all governance programs, effectively communicating the value and managing the program portfolio.

AI Governance, Ethics & Compliance  

• Lead the Responsible AI program, creating and enforcing policies for AI/ML model lifecycle, including fairness, bias mitigation, explainability (XAI), and transparency.
• Ensure global compliance with all relevant data privacy, security, and AI regulations (e.g., GDPR, CCPA, EU AI Act) in partnership with Legal, Compliance, and Security teams.
• Oversee the AI Risk Assessment process, proactively identifying and mitigating technical, ethical, and reputational risks associated with AI systems and their underlying data.

Execution & Organizational Change

• Build and scale the Data Stewardship and AI Governance bodies, defining roles and responsibilities and empowering a network of Data/AI Owners across business units.
• Drive Data/AI literacy and a cultural shift towards data ownership and responsible AI usage across the enterprise.
• Oversee the selection, implementation, and utilization of modern Data and AI Governance tools (e.g., Data Catalog, MDM, AI Risk Management platforms).

Leadership & Stakeholder Management

• Lead and mentor a high-performing global team of governance specialists, architects, and analysts.
• Serve as the primary liaison to the C-suite, providing clear, concise, and actionable updates on the state of Data & AI Governance, risk posture, and compliance.

Basic Qualifications

• Bachelor’s degree
• 12 plus years of progressive experience in Data Management, Data Governance, Information Technology
• 5 plus years in a senior leadership role managing global teams and complex, cross-functional programs.

Preferred Qualifications

• A master's degree or MBA.
• Deep practical knowledge of industry-leading data governance frameworks (e.g., DAMA-DMBOK, CDMC, DCAM) and their practical implementation in a large-scale enterprise environment.
• Strong understanding of the AI/ML model lifecycle, including data preparation, model training, deployment, and monitoring, and the specific governance challenges they present (e.g., model drift, bias).
• Expertise in current and evolving global data and AI regulations (GDPR, CCPA, EU AI Act, etc.) and how to translate legal requirements into technical and process controls.
• Experience with major cloud-based dat