About Amplify Health
Who We Are
Amplify Health is Asia's leading health technology and analytics organisation, providing our customers with integrated solutions to make healthcare more accessible, affordable and effective across the region.
We offer a unique B2B business model and integrated stack of SaaS-based products, PaaS-based HealthTech launchpad and DaaS-based on-demand data offerings to deliver impact to our customers across the healthcare value-chain.
Our joint-venture partners, AIA and Discovery, have provided us with the foundations and a platform that truly differentiates us from our competitors and allows us to build and deploy products at a scale and quality that few can match.
We aim to be the trusted custodian of Asia's largest repository of health data, unifying financial, clinical, operational and behavioural data to empower our customers with insights that highlight opportunities to deliver better value and care outcomes.
Our Vision and Ambition
To build the leading healthcare AI and platform services company in Asia that transforms the delivery of health and wellness for patients and communities by combining and leveraging the distinctive and complementary assets and strengths of AIA and Discovery.
Amplify Health will simplify access to health data and AI Innovation to accelerate distinct and disruptive healthcare value insights and resulting improvements in health outcomes through value-based care, personalised care plans and aligning individuals lifestyle/ behavioural choices.
By 2028, Amplify Health will have in place one of Asia's strongest health-tech and AI capabilities; a comprehensive, integrated health technology stack supported by precision insights derived from proprietary data pools.
The Position
Summary
We are hiring a Senior/ Lead Data Scientist to serve as a recognised organisational expert in Generative AI, Agentic AI, NLP and healthcare AI systems. This role shapes the Data & AI technical strategy and architecture, leads complex and high-impact AI initiatives, and establishes reusable, responsible AI capabilities across products, markets and business functions.
The ideal candidate combines exceptional depth in NLP, LLMs, RAG and agentic systems with broad architectural, data, analytics, product and healthcare domain judgement. The candidate defines how reliable AI systems should be designed, evaluated, governed and scaled, while developing the technical community through mentorship, standards and thought leadership.
Responsibilities
AI Strategy and Architecture
- Translate ambiguous business and healthcare problems into structured AI solutions with clear measurement frameworks, success metrics and delivery pathways.
- Define the enterprise technical strategy, reference architectures and capability roadmap for NLP, Generative AI, retrieval-augmented generation, multimodal AI and agentic systems.
- Act as the senior technical authority for high-risk and high-impact AI systems; make architecture trade-offs across model selection, prompting, fine-tuning, retrieval, tool use, memory, orchestration, latency, cost, security and maintainability.
- Lead technical due diligence for foundation models, embedding models, vector stores, orchestration frameworks and vendors, including build-versus-buy and model-risk decisions.
Production AI System Design
- Lead the design of production-grade AI systems where LLMs, RAG, NLP and agents are core runtime components of the delivered solution, not merely development aids.
- Define patterns for agent planning, tool use, permissions, state and memory, observability, fallback mechanisms, human-in-the-loop controls and safe failure handling.
- Shape reusable AI platforms, knowledge services, evaluation harnesses, guardrails and LLMOps capabilities in partnership with engineering, technology, cybersecurity, risk and product leaders.
- Clearly distinguish development-process automation from runtime AI-system behaviour: govern coding and analysis assistants as productivity tools while separately engineering and validating RAG, LLM and agentic components that operate within delivered AI systems.
Evaluation, Governance and Responsible AI
- Establish rigorous evaluation frameworks covering task quality, groundedness, retrieval performance, hallucination risk, safety, robustness, bias, privacy, latency, cost and human oversight.
- Define governance standards for AI system design, validation, monitoring, model risk, human accountability, auditability and responsible AI practices.
- Ensure AI systems are designed with appropriate controls for healthcare privacy, security, compliance, explainability, fairness and operational safety.
AI-Assisted Development and Productivity
- Champion the responsible adoption of modern AI-powered development tools, such as coding assistants, analytical copilots and platform-native AI assistants, to streamline model development, automate repetitive tasks, accelerate analytical insights and improve engineering productivity.
- Define approved usage patterns, quality controls, adoption metrics and value measures for AI-assisted development and process automation.
- Ensure productivity tools are adopted responsibly without compromising security, intellectual property, scientific quality or decision accountability.
Stakeholder Influence and Capability Building
- Influence executive strategy, investment and risk decisions by communicating evidence, uncertainty, trade-offs, expected impact and operational implications.
- Build organisational capability through mentorship, standards, training, communities of practice and external thought leadership.
- Develop senior technical talent and raise the overall quality of AI system design, scientific reasoning and production delivery across the function.
Candidate Profile
Required Qualifications
- 8–10+ years of experience developing and implementing end-to-end solutions using machine learning and AI technologies.
- 5+ years of hands-on experience in NLP, deep learning and transformer-based models.
- 3+ years of practical experience building end-to-end Generative AI solutions, including LLM workflows, fine-tuning, evaluation and RAG-based systems.
- 4+ years of experience leading, mentoring or managing high-performing data science teams is highly desirable.
- Strong proficiency in SQL, Python, PySpark, PyTorch, LangGraph, Databricks, MLflow, Azure AI services and LLM APIs is mandatory.
- Proven experience building, deploying and monitoring production-grade ML or AI systems at scale.
- Experience with LangChain and LangGraph for developing agentic AI and RAG-based applications.
- Strong understanding of GenAI observability, model tracing and LLM evaluation frameworks, including the ability to assess model quality, detect hallucinations, monitor performance and optimise production-grade AI systems through robust evaluation and monitoring practices.
- Knowledge of responsible AI practices, including assessment and monitoring of fairness, bias, explainability, safety, privacy and compliance metrics for production AI and GenAI solutions.
- Experience defining AI governance frameworks and measurable adoption practices for AI-assisted development tools and process automation.
- Demonstrated expertise and business acumen, with the ability to convert complex business problems into practical AI solutions that deliver measurable impact.
- Ability to influence executives and cross-functional leaders, and to develop senior technical talent.
- Excellent written and verbal communication skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders.
- Experience working in a matrix organisation.
- Demonstrated ability to work in a fast-paced, demanding environment while managing stakeholder expectations, priorities and timelines.
- Strong awareness of emerging technology trends in AI, Generative AI and Agentic AI, and the ability to assess what they mean for health tech products from both opportunity and risk perspectives.
Preferred Qualifications
- Bachelor's or master's degree in computer science or an equivalent technical field, with specialisation or experience in artificial intelligence, machine learning, data science or related disciplines.
- Experience in the healthcare domain is highly desirable, especially in areas such as clinical AI, payer/provider analytics, population health, claims, care management, medical operations or health data platforms.
- Experience working with healthcare data standards, privacy requirements, regulated environments or responsible AI considerations in healthcare.
- Hands-on experience with Azure, Databricks or equivalent cloud and data platforms.
- Strong knowledge of MLOps, CI/CD for ML, model monitoring, model governance and scalable deployment patterns.
- Demonstrated expertise in optimising AI systems for accuracy, performance, latency, reliability, cost and maintainability.
- Exposure to multimodal AI, knowledge graphs, medical text analytics or clinical decision support use cases is a plus.
What Good Looks Like
- Is recognised as the organisation's technical authority for complex AI, NLP and agentic-system decisions.
- Shapes enterprise AI strategy while remaining credible in hands-on architecture and technical review.
- Delivers AI systems that are measurable, grounded, safe, secure, scalable and economically sustainable.
- Creates reusable platforms and standards that multiply impact across teams, products and markets.
- Builds a strong technical community and credible external reputation in healthcare AI.
