The Assistant Director, Data Automation & ML Ops Lead will drive intelligent data automation, AI model operationalisation, and data governance initiatives at Tan Tock Seng Hospital. This leadership role involves managing a team of analysts and engineers to deliver innovative data solutions and AI-driven applications that improve clinical and operational outcomes. Responsibilities include: Leading and mentoring a team to deliver intelligent automation and AI solutions; Partnering with clinical and business stakeholders to design scalable data extraction and management solutions; Driving the deployment and adoption of AI and analytics models; Establishing governance, monitoring, and maintenance frameworks for deployed solutions; Leading the Trusted Third Party (TTP) function for data de-identification, anonymisation, and re-identification in compliance with security standards; Collaborating with internal and external partners to resolve technical issues; Staying current with emerging technologies like agentic AI and intelligent automation. Requirements: Degree in Computer Science, Engineering, Informatics, or related discipline; Minimum 8-10 years of experience in data management, engineering, analytics, or AI roles; Proven experience in stakeholder management and delivering data-driven solutions; Hands-on programming proficiency in Python and SQL; Experience in deploying and operationalising AI models; Strong leadership and analytical skills. Preferred qualifications include knowledge of data anonymisation techniques and familiarity with SharePoint, HTML, XML, VBA, and major cloud or enterprise platforms.
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Skills for this role
Data AutomationMLOpsData GovernanceData EngineeringAI Model OperationalisationData De-identificationData AnonymisationPythonSQLData AnalyticsStakeholder ManagementTeam LeadershipSharePointHTMLXMLVBAAgentic AI
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A focused preparation guide, built around this job’s responsibilities and requirements.
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Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Operationalizing AI and MLOps Frameworks
The role requires driving the full lifecycle of AI models, from deployment to ongoing monitoring and maintenance. You must demonstrate how you establish governance frameworks that ensure models remain reliable and effective in a clinical environment, moving beyond experimental prototypes to enterprise-wide production.
Leading the Trusted Third Party (TTP) Function
A critical responsibility is managing data de-identification, anonymisation, and re-identification services. You must prioritize compliance with strict data security standards while enabling responsible data access, ensuring that technical protocols for privacy are robust and audit-ready.
Stakeholder Engagement and Solution Design
You must bridge the gap between technical teams and clinical/operational stakeholders. The priority is translating complex business needs into scalable data extraction and management solutions, requiring strong communication skills to align technical delivery with organizational outcomes.
Likely challenges to think through
Balancing Innovation with Clinical Rigor
Implementing emerging technologies like agentic AI in a hospital setting involves high stakes regarding patient safety and data integrity. Prepare by discussing how you manage risk in technical deployments and ensure that new automation tools do not compromise existing clinical workflows or data security.
Navigating Complex Data Governance Requirements
The TTP function requires balancing the need for data accessibility with stringent privacy regulations. The challenge is maintaining efficient data workflows while adhering to evolving security protocols; prepare by articulating your experience with data lifecycle management and regulatory compliance frameworks.
03
Practice your answers
Technical practice
MLOps Lifecycle Management
What to coverDescribe your approach to establishing governance and monitoring frameworks for AI models in a clinical environment. Focus on how you ensure model reliability, performance drift detection, and security compliance throughout the deployment lifecycle.
Data De-identification and TTP Protocols
What to coverExplain the technical methodologies you would employ for data de-identification and anonymisation within a Trusted Third Party (TTP) function. Discuss how you balance the need for data utility with strict privacy and re-identification security standards.
Scalable Data Extraction and Management
What to coverHow do you design scalable data extraction pipelines that integrate with diverse clinical and operational systems? Address the trade-offs between batch processing and real-time data ingestion in a hospital setting.
Agentic AI and Intelligent Automation
What to coverHow do you evaluate the readiness of emerging technologies like agentic AI for enterprise adoption? Suggest a framework for piloting these tools while maintaining robust data governance and security.
Python and SQL in Data Engineering
What to coverDescribe a complex data engineering challenge you solved using Python and SQL. Emphasize your ability to write maintainable, efficient code that supports large-scale analytical or AI-driven applications.
Enterprise Platform Integration
What to coverDiscuss your experience integrating disparate technologies such as SharePoint, XML, or VBA with modern cloud-based data platforms. Explain how you manage technical debt while modernizing legacy reporting or automation workflows.
Translating Requirements to Technical Specifications
What to coverProvide an example of how you translate ambiguous clinical or business needs into concrete technical specifications. Focus on your documentation process and how you ensure alignment between stakeholders and engineering teams.
Data Governance Frameworks
What to coverWhat are the essential components of a data governance framework for a healthcare institution? Discuss how you enforce data quality, security, and access control policies across a multidisciplinary team.
Behavioural practice
Managing Cross-Functional Stakeholders
What to coverDescribe a situation where you had to align clinical and technical stakeholders on a data-driven initiative. Explain how you managed conflicting priorities and communicated technical constraints to non-technical partners.
Leadership and Team Development
What to coverProvide an example of how you have mentored data analysts or engineers to improve their technical output or professional growth. Detail the specific actions you took to foster a culture of innovation and accountability.
Driving Strategic Initiatives
What to coverTell us about a time you independently led a significant digital transformation project. Describe the obstacles you faced, how you navigated them, and the final impact on operational or clinical outcomes.
Resolving Technical Conflicts
What to coverDescribe a time you had to collaborate with external vendors or internal teams to resolve a critical technical issue. Focus on your role in facilitating the resolution and ensuring the long-term stability of the solution.
02
Sharpen your knowledge
Topics to prepare
Data Governance and Trusted Third Party (TTP) Protocols
Review frameworks for data de-identification, anonymisation, and re-identification. Exercise: Draft a hypothetical protocol for handling sensitive clinical data that ensures compliance with security standards while maintaining data utility for analytics.
MLOps and Model Lifecycle Management
Focus on the deployment, monitoring, and maintenance of AI models in production environments. Exercise: Design a monitoring strategy for an AI-driven clinical application to detect model drift and ensure ongoing reliability.
Agentic AI and Intelligent Automation
Explore the current landscape of agentic AI and its application in healthcare workflows. Exercise: Identify one operational bottleneck in a hospital setting and propose how an agentic AI solution could automate the process while maintaining human oversight.
Skills to revise
Python and SQL for Data Engineering
Demonstrate proficiency in building scalable data extraction and management pipelines. Exercise: Write a SQL query to join complex clinical datasets and a Python script to automate the cleaning and anonymisation of that data for reporting.
Stakeholder Management and Technical Translation
Practice translating complex business or clinical requirements into technical specifications. Exercise: Prepare a communication plan for presenting a new AI-driven solution to non-technical clinical stakeholders, focusing on impact and risk mitigation.
Enterprise Platform Integration
Familiarity with SharePoint, XML, and VBA for enterprise automation. Exercise: Create a simple VBA macro or XML schema to streamline a manual data entry task commonly found in administrative or clinical reporting.
04
Your preparation checklist
Prepare a case study on MLOps lifecycle management
Develop a structured walkthrough of a past project where you successfully deployed and maintained an AI model. Focus on the end-to-end process, including monitoring frameworks, version control, and how you ensured the solution remained reliable and effective in a production environment.
Draft a framework for Trusted Third Party (TTP) data governance
Outline a hypothetical or past protocol for handling sensitive data, specifically addressing de-identification, anonymisation, and re-identification techniques. Be prepared to explain how you balance data utility with strict security and privacy compliance requirements.
Develop a stakeholder engagement strategy
Prepare examples of how you have translated complex technical requirements into actionable solutions for non-technical clinical or business stakeholders. Highlight your approach to managing expectations and driving adoption of new data-driven tools.
Review technical proficiency in Python and SQL for automation
Be ready to discuss your experience using Python and SQL to build scalable data extraction and management pipelines. Prepare to explain how you have used these tools to automate manual processes or improve data reporting efficiency.
Formulate strategic questions for the interview panel
Prepare insightful questions regarding the current state of TTSH's data infrastructure, the primary challenges in their digital transformation agenda, and how the team currently balances innovation in agentic AI with existing governance frameworks.
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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