Senior Assistant Manager, Data Intelligence & AI

About the role

NHG is hiring a Senior Assistant Manager, Data Intelligence & AI (also described as Senior Data Analyst / AI Engineer) within the Division of Informatics & Data (DID). The role works closely with clinical domain experts, stakeholders and IT teams to design, build and deploy AI-powered solutions, run simulation modelling and solve optimisation problems. It sits between hands-on model development and strategic solution architecture, with a focus on cloud-native AI engineering and production-grade models that improve patient care.

Key responsibilities

• Lead end-to-end development of AI solutions, from data extraction, cleaning and preprocessing through model training, evaluation and deployment on cloud infrastructure (AWS, Azure or Hospital Private Cloud)
• Design and implement cloud-native data pipelines and MLOps workflows so models are versioned, monitored and maintained in production
• Work with clinicians, pharmacists and other healthcare professionals to turn domain needs into analytical solutions, promote adoption of model outputs in clinical workflows and review deployed models regularly
• Contribute to an institution-wide data science strategy and identify opportunities to integrate AI across functions
• Perform simulation modelling and optimisation using discrete-event simulation, linear/integer programming or metaheuristics
• Publish analytic work in journals and conferences, seek research collaborations and funding, and deliver training on AI, cloud computing and analytics
• Mentor junior data scientists and analysts
• Take part in organisational development and quality improvement, and work beyond office hours during emergencies such as infectious disease outbreaks

Required profile

• Degree or Master's in Data Science, Statistics, Computer Science, Computer Engineering or equivalent
• At least 3 years of experience developing data science solutions in a healthcare setting
• Ability to design and run complex experiments, manage large-scale data projects and think strategically about solution architecture
• Strong communication skills to bridge technical and clinical teams

Required skills

• Statistical modelling, machine learning and deep learning
• AI engineering: model serving, containerisation (Docker, Kubernetes) and CI/CD pipelines
• Cloud platforms with managed ML services such as SageMaker or Azure ML
• Python and/or R
• FlexSim simulation software is a value-add