New opportunity

Senior/ Data Analyst (1 Year Contract)

Ng Teng Fong General Hospital · Singapore

About this role

The Senior Data Analyst will serve as a technical lead within the Ng Teng Fong General Hospital, focusing on conceptualizing, planning, developing, and implementing Data and AI models to enhance healthcare services. The role involves evaluating care models using statistical methods, automating work processes to improve efficiency, and developing novel care paths through AI technologies. The successful candidate will also contribute to research writing and the publication of manuscripts. Responsibilities: Serve as the technical lead for healthcare AI and data projects, ensuring successful delivery of outcomes. Collaborate with clinicians to identify high-impact areas for AI interventions. Develop and maintain technical standards and best practices. Assume project lead responsibilities, including managing timelines, resources, deliverables, and grant management. Provide mentorship and guidance to junior team members. Requirements: Minimum Degree in Computer Science, Biomedical Informatics, AI, or a related field (MSc/PhD preferred). Minimum 3 years of working experience in data, IT, and AI-related fields. Proven track record of building and implementing Data Science, Machine Learning, and Generative AI models that have been operationalized at a corporate level or beyond. Preferred Qualifications: Experience in healthcare or research environments. Experience in leading teams. Experience in project management, grant management, and manuscript publishing.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Data ScienceMachine LearningGenerative AIAI ModelingProject ManagementGrant ManagementResearch WritingBiomedical Informatics

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Operationalizing AI and Data Models
The core mandate is to move beyond theoretical models to implement AI solutions that are actively used in a clinical or corporate setting. You must demonstrate a history of taking models from conceptualization through to deployment, specifically focusing on improving healthcare efficiency and developing novel care paths.
Clinical Collaboration and Translation
You will act as a bridge between technical data teams and clinicians. Success requires the ability to identify high-impact clinical areas for intervention and translate complex medical needs into actionable data science projects, ensuring that technical outputs directly support healthcare service improvements.
Technical Leadership and Mentorship
As a technical lead, you are expected to establish departmental standards and best practices for AI development. You must also provide guidance to junior staff, ensuring the team maintains high technical quality while managing project timelines and deliverables.
Research and Grant Management
The role requires active participation in the research lifecycle, including writing and publishing manuscripts. You must be prepared to handle the administrative and financial aspects of research, such as grant management and claims, to support the sustainability of healthcare projects.

Likely challenges to think through

Balancing Technical Rigor with Clinical Constraints
Healthcare environments often have strict regulatory and operational constraints that can limit the speed of AI deployment. Prepare by being ready to discuss how you have navigated data privacy, clinical safety, or resource limitations while still delivering functional, high-impact models.
Managing Short-Term Contract Objectives
As this is a one-year contract, there is inherent pressure to demonstrate value quickly. Prepare by focusing on how you prioritize high-impact, achievable milestones that can be operationalized within a limited timeframe without sacrificing long-term technical standards.
Translating Technical Work for Non-Technical Stakeholders
Collaborating with clinicians requires explaining complex AI and statistical concepts in a way that builds trust and buy-in. Prepare by practicing how you communicate the 'why' behind your model choices and how they directly address specific clinical pain points.

Practice your answers

Technical practice

Operationalizing Machine Learning Models

What to coverDiscuss your experience moving models from development to production in a corporate environment. Focus on the challenges of maintaining model performance, data drift, and ensuring reliability in a high-stakes setting like healthcare.

Generative AI in Healthcare

What to coverExplain how you would approach implementing Generative AI models to improve healthcare services. Address considerations regarding data privacy, model hallucination, and the specific ethical requirements of clinical environments.

Statistical Evaluation of Care Models

What to coverDescribe the statistical methods you use to evaluate the effectiveness of new care models. Explain how you ensure that the data-driven insights are statistically significant and actionable for clinical stakeholders.

Automating Clinical Workflows

What to coverProvide an example of how you have used data or AI to automate manual work processes. Focus on how you identified the bottleneck, the technical solution implemented, and how you measured the resulting efficiency gains.

Technical Standards and Best Practices

What to coverAs a technical lead, how do you establish and enforce coding standards, version control, and documentation practices within a data science team? Suggestion: Prepare to discuss how you balance rigorous standards with the need for rapid prototyping in research.

Biomedical Informatics Integration

What to coverHow do you ensure that AI models are integrated effectively with existing healthcare IT infrastructure? Discuss the importance of interoperability and data quality when working with clinical datasets.

Grant and Project Management

What to coverExplain your approach to managing project timelines and resources, specifically in the context of grant-funded research. Suggestion: Highlight your experience in tracking deliverables and managing claims to meet funding requirements.

Research Writing and Publication

What to coverDescribe your process for translating technical project outcomes into academic manuscripts. Focus on how you collaborate with clinicians to ensure the research findings are accurately represented for publication.

Behavioural practice

Collaborating with Non-Technical Stakeholders

What to coverDescribe a time you worked with clinicians to identify a high-impact AI intervention. Explain how you translated complex technical concepts into actionable goals that the clinical team could support.

Mentoring Junior Team Members

What to coverProvide an example of how you have provided guidance to a junior data analyst. Focus on how you balanced their professional development with the need to meet project deadlines.

Managing Conflicting Project Priorities

What to coverTell us about a time you had to manage multiple project deliverables simultaneously. Explain how you prioritized tasks, managed resources, and communicated progress to stakeholders.

Navigating Technical Challenges in Research

What to coverDescribe a situation where a research project faced a significant technical hurdle. Explain the actions you took to resolve the issue and the final impact on the project's success.

Sharpen your knowledge

Topics to prepare

Healthcare AI Model Evaluation
Review statistical methods for evaluating clinical care models. Exercise: Prepare a case study on how you would validate an AI model's performance metrics against clinical outcomes to ensure it improves patient care efficiency.
Research Manuscript Development
Understand the structure of academic research papers in biomedical informatics. Exercise: Draft an abstract for a hypothetical AI-driven healthcare intervention, focusing on methodology, data privacy considerations, and clinical impact.
Grant and Project Lifecycle Management
Familiarize yourself with the administrative requirements of healthcare research grants. Exercise: Create a project roadmap that aligns technical milestones with grant reporting deadlines and resource allocation.

Skills to revise

Operationalizing Generative AI
Focus on moving GenAI models from prototype to production in a corporate environment. Exercise: Outline a deployment strategy that addresses data security, model monitoring, and integration with existing hospital workflows.
Technical Leadership and Mentorship
Practice articulating technical standards and guiding junior staff. Exercise: Develop a set of best practices for code documentation and model versioning that you would implement to ensure team consistency.
Clinical Collaboration
Develop techniques for translating clinical needs into technical requirements. Exercise: Simulate a requirements-gathering session with a clinician to identify a high-impact area for an AI intervention, focusing on defining success metrics.

Your preparation checklist

Prepare a portfolio of operationalized AI models
Select 2-3 projects where you built and deployed Data Science, Machine Learning, or Generative AI models. Prepare to explain the technical architecture, how you ensured the model was operationalized at a corporate level, and the specific measurable impact on efficiency or care outcomes.
Draft a research and publication summary
Compile a list of your past research contributions, manuscripts, or technical papers. Be ready to discuss your role in the writing process and how you translate complex clinical or technical data into publishable research findings.
Develop a project management and grant framework
Create a brief outline of your experience managing project lifecycles, specifically focusing on resource allocation, timeline management, and grant administration. Prepare examples of how you have handled grant claims or reporting requirements in previous roles.
Formulate questions on clinical collaboration
Prepare thoughtful questions for the interviewers regarding how the hospital identifies high-impact areas for AI interventions and how the data team bridges the communication gap between technical developers and clinical staff.
Outline a mentorship and standards strategy
Prepare to discuss your approach to establishing technical standards and best practices within a team. Provide concrete examples of how you have mentored junior staff or improved team technical workflows in your previous roles.

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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