Overview

The Assistant Director / Senior Manager, AI Workflow Implementation will lead the institute’s implementation of AI-enabled workflows across research, scientific administration and leadership functions.

The role will work closely with other relevant offices like the A*STAR Scientific Data Strategy Office as well as the scientific leadership in IMCB to align on organisation data strategy while developing AI use cases that would deliver on IMCB's scientific strategy as well as data and AI ambitions. The role will focus on translating institutional scientific needs into practical AI workflows, coordinating implementation across stakeholders, and ensuring that AI tools are adopted safely, effectively and sustainably.

This is a senior implementation and coordination role.

Key Responsibilities

1. AI workflow implementation

• Lead the identification, scoping and prioritisation of AI workflow opportunities across research groups & platforms, with inputs from the scientific leadership.
• Translate institutional and scientific needs into clear AI workflow requirements, including users, source data, expected outputs, review points, risks and success measures.
• Oversee AI pilots from scoping through testing, implementation, adoption and post-implementation review.
• Coordinate with technical partners, vendors or internal teams to support delivery of AI-enabled tools and workflows.
• Establish practical workflow playbooks for scientific data, templates and implementation guidance for responsible and consistent AI use for biomedical research.
• Evaluate pilot outcomes and recommend whether to scale, refine or stop specific AI workflows.

2. Data strategy alignment

• Work closely with the Scientific Data Strategy Office to align AI workflow implementation with institute data strategy, governance and data readiness priorities.
• Translate data strategy priorities into practical implementation requirements for AI-enabled workflows.
• Oversee mapping of data sources, data owners, access needs, metadata requirements and data quality considerations for AI use cases.
• Ensure AI workflow pilots follow agreed principles on data access, confidentiality, source traceability, human review and appropriate use of institutional data.
• Identify data readiness gaps that affect AI implementation and escalate these to the relevant data, IT or governance owners.

3. Stakeholder and governance coordination

• Engage senior scientific, administrative and leadership stakeholders to identify high-value use cases and adoption barriers.
• Coordinate with PIs, platform leads, scientific teams, corporate functions, IT, legal, data governance and external partners.
• Support the development of institute-level guidance on responsible AI use in partnership with relevant governance owners.
• Manage vendor or external partner contributions to ensure alignment with institute requirements, timelines and governance expectations.

4. Adoption and impact

• Oversee user engagement, testing, training and feedback processes for approved AI workflows.
• Define practical success measures, such as time saved, improved retrieval, reduced manual work, better knowledge access or improved consistency of outputs.
• Track implementation progress, adoption, user satisfaction, risks and practical impact.

Requirements

Essential

• Masters, PhD preferred, in computer science, data science, information systems, engineering, life sciences, biomedical sciences or a related field.
• At least 6 years of relevant experience in AI implementation, digital transformation, data-enabled workflow improvement, technology programme delivery or related areas.
• Working knowledge of AI tools and implementation concepts, including large language models, retrieval-based systems, workflow automation, AI-enabled scientific tools or enterprise AI platforms.
• Strong understanding of data governance concepts, including data ownership, access control, metadata, confidentiality, source traceability and human review.
• Demonstrated experience leading cross-functional implementation projects involving technical and non-technical stakeholders.
• Ability to engage senior scientists, administrators, IT teams, data strategy teams, governance stakeholders and external partners.
• Strong judgement in assessing use-case value, data readiness, implementation risk and adoption feasibility.
• Strong written communication, presentation and stakeholder management skills.

Preferred

• • Experience implementing AI, digital or data-enabled workflows in research, healthcare, biotech, pharma, public sector, higher education or another knowledge-intensive environment.
  • Familiarity with biological research environments, scientific workflows or research institute operations.
  • Prior experience working with data strategy, data governance, knowledge management, research intelligence or digital adoption teams.
  • Experience managing vendors, consultants or technical delivery partners.
  • Ability to translate institutional strategy into practical implementation plans.
  • Experience preparing senior management updates, governance papers or implementation roadmaps.

Why Join IMCB

This role offers the opportunity to directly shape how AI and digital tools are embedded into the day-to-day operations and communications of a leading biomedical research institute, with potential to grow into broader digital and strategy leadership.