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Singapore, Singapore
Hybrid
We are a growing regional digital bank group and are revolutionizing financial banking services across Southeast Asia. Our mission is to unlock big dreams and drive financial inclusion throughout the region. As a regional digital bank, we have the right foundation—data, technology, and trust—because we are Build With Heart. We believe that real impact starts with real people. If you're ready to Own The Mission and help us shape the future of Digital banking, we invite you to join us.
About the Role:
Responsible for creating and driving data and AI governance initiatives for a digital bank that seeks to offer inclusive banking services to customers based on a strong foundation of trusted data and AI innovation.
As the Group Data & AI Governance Specialist, you will be involved in creating and implementing data and AI governance policies, processes, systems, and tools, as well as influencing the Bank Group's data and model architecture to ensure enterprise data and AI assets are securely stored, documented, deployed, and managed effectively. You will partner with the data science, MLOps, product, technology and risk teams to drive initiatives that embed privacy, accountability and fairness into the Bank Group's products and process design, providing expert advice on compliance with data regulations and evolving responsible AI requirements.
Partner with data engineering and MLOps teams to implement a robust data architecture that ensures enterprise data is securely stored and managed effectively, and data governance solutions, metrics, reports and dashboards to improve efficiency, data accuracy and consistency.
Implement and facilitate the aggregation of data quality and AI controls and metrics, using governance tools, reports and dashboards.
Implement data governance solutions using APIs, automation, and other technologies (e.g. Generative AI) in collaboration with IT, Data Engineering and IAM teams, to improve efficiency and accuracy in data governance processes, enforcement of data access controls, data retention and security measures to protect sensitive data.
Establish and govern the lifecycle for traditional ML, Generative AI and agentic systems from ideation to production. Define frameworks policies and processes to assess, mitigate and monitor AI risks throughout the AI lifecycle.
Develop clear technical guidance, documentation, and best practices for Bank Group employees on data and AI governance issues and responsible AI deployment.
Facilitate and respond to data and AI governance-related enquiries, complaints, and audits. Act as a key coordinator in managing and remediating data and AI incidents.
Working with Data Stewards, Data Owners and relevant parties to implement and enforce the enterprise-wide data governance framework, including policies, standards, and procedures set by the EDaC.
Collaborate with Data Steward to define and maintain critical data elements and translate business rules into technical data quality rules in accordance with the established Data Quality Dimensions.
At least 3 years of relevant experience with a strong understanding of AI technology, including AI model validation, ML risk assessments, model monitoring, and data governance activities/processes, preferably in the banking, financial services, or digital services sector in Singapore.
Knowledge and hands-on experience of the AI and ML development lifecycle (data preparation, feature engineering, training, testing, deployment, and monitoring).
Ability to adopt practical, risk-based approaches to effectively balance innovative business and AI development velocity with strict compliance requirements.
Ability to thoroughly understand the business and technical requirements of AI, LLMs, and large-scale data systems, and manage the technical implementation of governance, observability, and risk management systems and software tools.
Sound understanding and technical experience in AI and data management, including data governance principles, metadata management, model lineage, data access, data quality management and data stewardship best practices.
Familiarity with data warehousing concepts, preferably within Snowflake.
Good understanding of data and AI governance-related regulatory requirements and industry standards, especially in the area of traditional AI, GenAI, and agentic AI (e.g., MAS FEAT principles and AI Risk Management Framework, IMDA Model AI Governance Framework). A good understanding of financial sectoral laws and other related regulations is highly preferred.
Self-starter, able to work independently and collaboratively across multi-functional teams.
Industry Knowledge: Experience in the banking or financial services industry. Deep understanding of banking data domains (e.g., customer KYC, AML/CFT, credit risk, deposits, payments).
Strong project management, communication, interpersonal, negotiation skills and experience working in an Agile/Scrum development environment.