Requirements/Qualifications

• PhD in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
  
• At least 4 years postdoctoral experience in managing complex research data and software/cloud systems as well as modelling in machine learning artifacts.
  
• Minimum of 7 years’ experience in designing data collection and integration architectures and software/cloud solutions.
  
• Previous experience at an international/ regional research organization is an added advantage.
  

• Manage and coordinate all research on-premises and cloud infrastructure with respective data and software systems to ensure the overall maintenance and evolution.
  
• Contribute to resource mobilization; grant writing, and proposal development to secure sustainable funding for data-driven research and software/cloud infrastructure development.
  
• Be the corporate leader in data management and work with staff to establish a vision and culture that embraces data management and accountability as a cross-corporate asset.
  
• Develop and align the institutional data management policy with relevant regulatory, legal, and ethical mandates.
  
• Develop and maintain data management standards for the Centre and regularly review emerging data management tools and skill and ensure timely introduction to the Centre.
  
• Supervise the development of relevant databases, web systems, and other software for the collection, storage, and distribution of research data.
  
• Develop and maintain data/software audit trails, controls on data quality and interoperability to manage corporate data related risks.
  
• Manage programmes that advance the roles of data management and oversee the integrity, security, usage and availability of institutional data management policy while ensuring compliance with data governance, protection, privacy, and security requirements across systems and architectures.
  
• Advance modelling and conduct research to implement optimized/novel artificial intelligence and machine learning (including large language models) initiatives and solutions.
  
• Deploy internal and external-facing artificial intelligence applications using cutting-edge data science and software engineering practices.
  
• Coordinate diverse scientific project components that require a centralized data clearing house to ensure interoperability and data accessibility.
  
• Optimize cloud resources and products for performance and cost-effectiveness.
  
• Develop and facilitate in-house data management training courses and the use of R and
  
• Python statistical software for scientists and students.
  
• Enhance capacity building by supervising and mentoring software developers, data analysts, and (post)graduate students.
  
• Contribute and lead the drafting and publishing of high-impact manuscripts.