Head of AI and Automation

RESPONSIBILITIES/RESULT AREAS

1. Provide strategic leadership for the AI and Automations pillar, including Data Science, MLOps, and Intelligent Automation functions.
   

2. Drive delivery of AI and Intelligent Automation use cases aligned with the Implementation Matrix priorities.
   

3. Establish AI governance frameworks, model risk management practices, and ethical AI principles.
   

4. Lead development of AI proofs-of-concept (POCs) and oversee transition to production readiness.
   

5. Build partnerships with universities, research institutions, and technology vendors for AI innovation.
   

6. Manage budget allocation across AI research, development, and deployment activities.
   

7. Represent the Centralized Data Office in AI-related forums, committees, and external engagements.
   

8. Ensure all AI and Intelligent automation solutions meet regulatory, ethical, and technical standards.
   

9. Develop and maintain the AI capability roadmap and technology stack.
   

10. Foster a culture of experimentation, learning, and responsible innovation.
    

11. Any other duties that may be assigned by the management and supervisor.
    

ACADEMIC AND PROFESSIONAL QUALIFICATIONS AND KNOWLEDGE

1. Bachelor’s Degree in a quantitative or technical field such as Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics, Information Systems, or a related discipline.
   

2. Master's Degree in a quantitative or technical field such as Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics, Information Systems, or a related discipline. 
   

3. PhD in Artificial Intelligence, Machine Learning, Data Science, Intelligent Automation or a related discipline is added advantage.
   

YEARS AND NATURE OF EXPERIENCE

1. Minimum of ten (10) years' experience in Artificial Intelligence, Machine Learning, Data Science and Intelligent Automation or related disciplines.
   

2. At least five (5) years' experience leading the design, development, deployment, and operationalization of enterprise Artificial Intelligence, Analytics, or Intelligent Automation solutions.
   

3. Minimum of three (3) years' experience managing and developing multi-disciplinary technical teams comprising Artificial Intelligence, Machine Learning, Automation, Data Science, and Data platform professionals.
   

4. Proven experience working within Data & Analytics, Artificial Intelligence, Business Intelligence or Intelligent Automation functions, delivering classic AI, Generative AI, and Intelligent Automation solutions within a highly regulated environment such as central banking, financial services, government, telecommunications or regulatory institutions.
   

5. Demonstrated expertise in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing (NLP), Large Language Models (LLMs), Generative AI, AI Agents, and Retrieval-Augmented Generation (RAG).
   

6. Proven experience implementing Intelligent Automation solutions using Robotic Process Automation (RPA), workflow automation, business process orchestration, and low-code automation platforms such as Power Automate, UiPath, Automation Anywhere, Blue Prism, or equivalent technologies.
   

7. Hands-on experience deploying AI solutions into production environments and managing the full AI lifecycle, including model development, testing, deployment, monitoring, retraining, and performance optimisation.
   

8. Experience designing enterprise AI and automation architectures, including integration with business applications, APIs, enterprise data platforms, and cloud environments.
   

9. Good knowledge of cloud-based AI platforms such as Azure AI, Azure Machine Learning, Azure OpenAI, Databricks, AWS AI/ML, Google AI, or equivalent enterprise platforms.
   

10. Experience implementing controls for AI model validation, monitoring, explainability, security, privacy, and compliance.
    

11. Familiarity with international standards and frameworks such as ISO 42001, ISO 27001, NIST AI Risk Management Framework, EU AI Act, or equivalent governance frameworks.
    

RESPONSIBILITIES/RESULT AREAS

1. Provide strategic leadership for the AI and Automations pillar, including Data Science, MLOps, and Intelligent Automation functions.
   

2. Drive delivery of AI and Intelligent Automation use cases aligned with the Implementation Matrix priorities.
   

3. Establish AI governance frameworks, model risk management practices, and ethical AI principles.
   

4. Lead development of AI proofs-of-concept (POCs) and oversee transition to production readiness.
   

5. Build partnerships with universities, research institutions, and technology vendors for AI innovation.
   

6. Manage budget allocation across AI research, development, and deployment activities.
   

7. Represent the Centralized Data Office in AI-related forums, committees, and external engagements.
   

8. Ensure all AI and Intelligent automation solutions meet regulatory, ethical, and technical standards.
   

9. Develop and maintain the AI capability roadmap and technology stack.
   

10. Foster a culture of experimentation, learning, and responsible innovation.
    

11. Any other duties that may be assigned by the management and supervisor.
    

ACADEMIC AND PROFESSIONAL QUALIFICATIONS AND KNOWLEDGE

1. Bachelor’s Degree in a quantitative or technical field such as Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics, Information Systems, or a related discipline.
   

2. Master's Degree in a quantitative or technical field such as Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics, Information Systems, or a related discipline. 
   

3. PhD in Artificial Intelligence, Machine Learning, Data Science, Intelligent Automation or a related discipline is added advantage.
   

YEARS AND NATURE OF EXPERIENCE

1. Minimum of ten (10) years' experience in Artificial Intelligence, Machine Learning, Data Science and Intelligent Automation or related disciplines.
   

2. At least five (5) years' experience leading the design, development, deployment, and operationalization of enterprise Artificial Intelligence, Analytics, or Intelligent Automation solutions.
   

3. Minimum of three (3) years' experience managing and developing multi-disciplinary technical teams comprising Artificial Intelligence, Machine Learning, Automation, Data Science, and Data platform professionals.
   

4. Proven experience working within Data & Analytics, Artificial Intelligence, Business Intelligence or Intelligent Automation functions, delivering classic AI, Generative AI, and Intelligent Automation solutions within a highly regulated environment such as central banking, financial services, government, telecommunications or regulatory institutions.
   

5. Demonstrated expertise in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing (NLP), Large Language Models (LLMs), Generative AI, AI Agents, and Retrieval-Augmented Generation (RAG).
   

6. Proven experience implementing Intelligent Automation solutions using Robotic Process Automation (RPA), workflow automation, business process orchestration, and low-code automation platforms such as Power Automate, UiPath, Automation Anywhere, Blue Prism, or equivalent technologies.
   

7. Hands-on experience deploying AI solutions into production environments and managing the full AI lifecycle, including model development, testing, deployment, monitoring, retraining, and performance optimisation.
   

8. Experience designing enterprise AI and automation architectures, including integration with business applications, APIs, enterprise data platforms, and cloud environments.
   

9. Good knowledge of cloud-based AI platforms such as Azure AI, Azure Machine Learning, Azure OpenAI, Databricks, AWS AI/ML, Google AI, or equivalent enterprise platforms.
   

10. Experience implementing controls for AI model validation, monitoring, explainability, security, privacy, and compliance.
    

11. Familiarity with international standards and frameworks such as ISO 42001, ISO 27001, NIST AI Risk Management Framework, EU AI Act, or equivalent governance frameworks.