Consortium for Clinical Research and Innovation, Singapore (CRIS) · Singapore
About this role
The Consortium for Clinical Research and Innovation, Singapore (CRIS) is seeking a Senior Manager for Product Management to lead the SIMFONI (SIngapore Medical FOundation AI model) programme. This role focuses on the research, development, integration, and evaluation of AI reasoning models within the public healthcare system, specifically targeting clinical decision support for cardiometabolic conditions. Responsibilities include defining product vision and strategy, leading Agile governance across multidisciplinary teams, managing complex stakeholder relationships, and ensuring system readiness for clinical deployment. The role requires embedding responsible AI principles, privacy safeguards, and regulatory compliance (e.g., SaMD) from the outset. Requirements: At least 6 years of experience in product management or leadership, ideally in healthcare, AI, or public sector innovation. Proven track record in managing multi-stakeholder programs from research to deployment. Strong understanding of Agile principles and healthcare delivery systems. Preferred qualifications include a degree in computer science, engineering, or public health, Agile certifications (CSPO, SAFe), and experience with AI ethics or large-scale digital health rollouts. This is a 3-year renewable contract position.
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A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Clinical AI Product Strategy and Roadmap
The primary objective is to translate complex clinical and research requirements for cardiometabolic conditions into a coherent product vision. You must prioritize the development of AI reasoning models that are not only technically sound but also clinically actionable, ensuring the roadmap bridges the gap between research-led POCs and scalable national healthcare deployment.
Multi-Stakeholder Governance and Alignment
Success depends on your ability to act as a central liaison between clinicians, technical AI researchers, and public sector regulatory bodies. You must manage competing interests and align diverse workstreams under a unified Agile governance framework to ensure milestones are met across the entire SIMFONI program.
Regulatory Compliance and Responsible AI Integration
You are responsible for embedding privacy, ethics, and safety into the product lifecycle from the start. This requires proactive management of SaMD (Software as a Medical Device) classification, data governance, and transparency standards to ensure the AI tools are fit for clinical use within the public healthcare system.
Likely challenges to think through
Bridging Research Innovation with Clinical Rigor
A significant challenge is the tension between the experimental nature of AI foundation models and the strict safety requirements of clinical environments. Prepare by demonstrating how you manage the trade-off between rapid iteration and the high-stakes need for clinical validation, data standardization, and evidence-based performance.
Navigating Public Sector Healthcare Complexity
Implementing AI in a national healthcare system involves navigating entrenched workflows and diverse institutional stakeholders. Prepare by articulating your approach to change management and system integration, specifically how you plan to gain clinician buy-in while ensuring the technology remains interoperable with existing health IT infrastructure.
03
Practice your answers
Technical practice
Managing SaMD Regulatory Pathways
What to coverExplain your experience navigating Software as a Medical Device (SaMD) classifications. Discuss how you integrate regulatory documentation and compliance requirements into the product development lifecycle without stalling Agile delivery.
AI Foundation Model Integration in Clinical Workflows
What to coverHow do you approach the challenge of integrating AI reasoning models into existing clinical decision support systems? Address how you balance technical model performance with the practical usability requirements of healthcare professionals.
Data Standardization and Clinical Terminology
What to coverDescribe your approach to standardizing disparate datasets for AI training. Discuss the importance of clinical terminology mapping and how you ensure data quality and interoperability in a large-scale healthcare environment.
Implementing Responsible AI and Privacy Safeguards
What to coverHow do you embed ethics, transparency, and patient privacy into the product roadmap? Provide examples of how you have addressed bias or accountability concerns when developing AI tools for public health.
Agile Governance for Research-Heavy Projects
What to coverHow do you adapt Agile methodologies when the product involves high-uncertainty research and development? Explain how you manage sprint planning and prioritization when outcomes are dependent on evolving research inputs.
Scaling Prototypes to Clinical Deployment
What to coverWhat are the key considerations when transitioning an AI proof-of-concept to a national-scale clinical deployment? Focus on system integration, change management, and ensuring long-term sustainability.
Defining Product Vision for Public Health AI
What to coverHow do you translate high-level national healthcare priorities into a concrete product roadmap? Discuss your process for identifying which AI-enabled tools will provide the most significant impact for cardiometabolic condition management.
Behavioural practice
Managing Conflicting Stakeholder Interests
What to coverDescribe a time you had to align clinicians, technical researchers, and public sector stakeholders on a shared product goal. How did you resolve strategic trade-offs when their priorities were in direct conflict?
Leading Through Ambiguity in Innovation
What to coverProvide an example of a complex, multi-party program you led from research to deployment. How did you maintain momentum and clarity of vision when faced with significant technical or regulatory uncertainty?
Facilitating Cross-Functional Collaboration
What to coverTell me about a time you had to bridge the gap between technical teams and non-technical clinical staff. What specific communication or facilitation techniques did you use to ensure mutual understanding and project alignment?
Adapting to Changing Project Requirements
What to coverDescribe a situation where research findings necessitated a significant pivot in your product strategy. How did you manage the communication of this change to stakeholders and ensure the team remained focused on the new objectives?
02
Sharpen your knowledge
Topics to prepare
Software as a Medical Device (SaMD) Regulatory Frameworks
Review the IMDRF guidelines and local Singaporean regulatory requirements for AI-enabled clinical decision support systems. Exercise: Create a high-level compliance checklist for a hypothetical AI model deployment, mapping specific features to risk-based classification categories.
Responsible AI and Ethics in Healthcare
Understand the principles of transparency, accountability, and bias mitigation in clinical AI. Exercise: Draft a framework for evaluating the fairness and safety of a foundation model used in cardiometabolic care, focusing on how to communicate these risks to non-technical clinical stakeholders.
Clinical Workflow Integration
Study how AI reasoning models interface with existing Electronic Medical Records (EMR) and clinical decision support systems. Exercise: Map a patient journey for a cardiometabolic condition and identify specific touchpoints where an AI model could provide actionable insights without disrupting clinical practice.
Skills to revise
Agile Governance for Research-to-Deployment
Apply Agile methodologies to manage the transition from research-heavy AI development to clinical proof-of-concept. Exercise: Design a sprint cadence that balances exploratory research tasks with structured product delivery milestones, ensuring alignment across multidisciplinary teams.
Clinical Data Standardization and Mapping
Gain proficiency in standardizing disparate health datasets for AI training. Exercise: Review common clinical terminology standards (e.g., SNOMED CT, LOINC) and practice mapping a sample dataset to ensure interoperability for a hypothetical AI model training pipeline.
Multi-Stakeholder Program Management
Develop strategies to align clinicians, researchers, and public sector policy makers. Exercise: Prepare a communication plan for a project steering committee that addresses conflicting priorities between technical feasibility and clinical utility.
04
Your preparation checklist
Develop a case study on AI in clinical decision support
Prepare a structured walkthrough of a past project where you managed the transition of an AI or digital health tool from research/POC to clinical deployment, specifically highlighting how you navigated regulatory requirements like SaMD or data governance.
Map your experience to the SIMFONI ecosystem
Create a brief document outlining your experience with cardiometabolic conditions or similar chronic disease management, and explain how you would approach standardizing datasets and clinical terminology mapping for a foundation model.
Draft an Agile governance framework for multidisciplinary teams
Outline a proposed Agile delivery model that balances the iterative nature of AI research with the rigid safety and compliance requirements of a public healthcare system, demonstrating your ability to lead diverse stakeholders.
Prepare questions on regulatory and ethical integration
Formulate thoughtful questions regarding the current state of the SIMFONI regulatory roadmap, specifically how the team balances rapid AI innovation with the stringent privacy and safety standards required for public health deployment.
Synthesize your stakeholder management strategy
Prepare a summary of how you have previously aligned conflicting interests between technical researchers, clinicians, and policy makers to achieve a shared product vision, using a specific example of a high-stakes trade-off you resolved.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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