• The Senior Artificial Intelligence Officer is responsible for defining, executing, and governing Singlife’s enterprise GenAI strategy. This involves driving the delivery of measurable business outcomes while meeting regulatory, ethical, and risk management standards.
• This role is critical in embedding GenAI as a core capability across Singlife’s distribution, underwriting, customer engagement, and operations, while ensuring compliance with MAS guidelines and internal Responsible AI frameworks.
• The Senior AI Officer owns end‑to‑end GenAI strategy, governance and implementation, bridging business, technology, and risk functions.

• Enterprise owner of Singlife's Artificial Intelligence and Generative AI strategy, governance, and operating model.
• Responsible for embedding AI and GenAI capabilities across Distribution, Underwriting, Customer Engagement, Operations, and Corporate Functions.
• Accountable for AI governance, model risk management, and compliance with applicable regulatory requirements and internal Responsible AI frameworks.
• Executive sponsor for enterprise AI literacy, capability building, and adoption initiatives.

• Define Singlife’s GenAI strategy and roadmap aligned with enterprise priorities.
• Identify, prioritize, and scale high-value GenAI use cases.
• Translate GenAI into measurable outcomes, including revenue uplift, cost efficiency, and customer / advisor metrics.
• Drive GenAI investment prioritization and business case evaluation

• Lead end-to-end GenAI lifecycle: use case design, development, deployment, monitoring, and scaling
• Establish enterprise AI platforms (e.g., cloud AI, GenAI services, MLOps/LLMOps)
• Define and institutionalize Singlife’s GenAI operating model

Technical Expertise in GenAI

• Identify emerging AI technologies and partnerships to drive competitive advantage.
• Evaluate and manage vendors and platforms (GenAI, automation, analytics)

Cross-Functional Leadership & Transformation

• Drive AI adoption across business units.
• Align senior stakeholders on AI priorities, investments, and progress.

Talent , Culture & AI Literacy

• Lead high-performing AI, data science, and AI engineering teams.
• Drive enterprise AI literacy and upskilling across business functions.
• Foster a culture of responsible experimentation and data-driven decision making.

AI Governance, Risk & Regulatory Compliance

• Establish and maintain AI governance frameworks covering model lifecycle, controls, and auditability
• Ensure compliance with MAS regulations, data privacy, and internal Responsible AI principles
• Oversee model risk management (bias, explainability, fairness, security, robustness)

Team

• Direct accountability for AI Strategy, Data Science, AI Engineering, and AI Governance resources.
• Matrix leadership across business units to drive enterprise AI adoption and transformation.
• Responsible for building future AI capabilities and talent pipelines across the organization.

Requirements

Experience

• Minimum 10 years of experience in data, analytics, digital transformation, technology strategy, or related fields, including leadership responsibilities.
• Demonstrated experience leading enterprise transformation initiatives and delivering measurable business outcomes.
• Experience deploying AI, machine learning, or Generative AI solutions at scale.
• Strong commercial acumen with the ability to translate technology investments into business value.
• Proven track record in stakeholder management and influencing senior executives.
• Deep understanding of AI governance, model risk management, regulatory compliance, and responsible AI principles.
• Prior experience within insurance, financial services, or regulated industries is strongly preferred.
• Experience leading high-performing teams comprising data scientists, engineers, product managers, and business stakeholders.

Education

• Academic: Bachelor's degree in Computer Science, Engineering, Data Science, Analytics, Mathematics, or a related discipline.
• Advanced Degree: Master's degree in Artificial Intelligence, Data Science, Engineering, Business Administration, or a related field preferred.

Key Stakeholder

External

• Regulators including the Monetary Authority of Singapore (MAS)
• AI technology providers, cloud service providers, and strategic partners
• Consulting firms, industry bodies, and professional associations
• External auditors and independent reviewers

Internal

• Executive Leadership Team
• Business Unit Heads
• Technology and Digital Teams
• Data & Analytics Teams
• Risk Management
• Compliance and Legal
• Operations and Customer Experience Teams
• Human Resources and Learning & Development

Success Metrics

• Business value delivered through AI initiatives, including revenue uplift, productivity improvements, and cost savings.
• Percentage of AI use cases successfully scaled into production.
• AI adoption rates across business units.
• Model performance, uptime, reliability, and operational effectiveness.
• Compliance with MAS requirements and internal AI governance frameworks.
• AI talent development, capability uplift, and enterprise AI literacy outcomes.
• Stakeholder satisfaction and business impact realization.