Senior Data Scientist (ML & Advanced Analytics)

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.

Position Summary We are hiring a Senior/ Lead Data Scientist who will serve as a recognised organisational expert in machine learning, decision science and advanced analytics. This role will shape the Data & AI technical strategy, solve the most complex data problems, lead innovation across portfolios and establish reusable capabilities that materially improve healthcare decisions, operational outcomes and business performance. The ideal candidate combines exceptional depth in statistical learning, machine learning and decision science with strong architectural, data, analytics, product and healthcare domain judgement. The candidate should be able to lead through influence as a technical leader, define enterprise standards and create reusable analytical and ML capabilities across markets and product lines.

Responsibilities Strategy, Architecture and Technical Leadership

• Define the strategic direction, reference architectures and capability roadmap for machine learning and advanced analytics across priority healthcare use cases.
• Act as the senior technical authority for complex or high-risk data science problems; challenge assumptions, select appropriate methods and make trade-offs across value, risk, interpretability, scalability and time to market.
• Lead the design of enterprise-grade analytical approaches spanning predictive modelling, forecasting, causal inference, fraud detection, risk benchmarking, personalisation, optimisation, decision intelligence and model-driven products.
• Lead technical due diligence and evaluation of emerging methods, platforms and vendor capabilities; distinguish durable business value from experimentation hype.

Scientific Standards, Governance and Responsible AI

• Create standards for scientific validation, uncertainty quantification, explainability, fairness, transportability, model risk, governance, monitoring and evidence required for material decisions.
• Sponsor the responsible adoption of AI-assisted development tools and agentic workflow automation across the function; define approved patterns, controls, benchmarks and value measures.
• Design an operating model in which agents automate repeatable process stages, including data profiling, exploratory data analysis, feature discovery, experiment orchestration and model documentation, while expert review gates preserve accountability for the final ML model, business recommendation and decision outcome.

Platforms, Reusability and Scalable Delivery

• Shape reusable data, feature, modelling, evaluation and monitoring platforms in partnership with engineering, architecture, security and product leaders.
• Establish reusable analytical assets, feature patterns, model evaluation frameworks and monitoring standards that can scale across markets, products and healthcare use cases.
• Influence platform and MLOps priorities to improve speed, reliability, governance and production readiness of data science solutions.

Stakeholder Influence and Organisational Capability

• Influence executive decisions and portfolio priorities by communicating evidence, uncertainty, strategic options and expected impact.
• Build organisational capability through mentorship, communities of practice, training, external thought leadership and partnerships with academia and industry.
• Provide technical coaching to data scientists and analytics teams, helping raise the overall quality of scientific thinking, delivery standards and business impact.

Candidate Profile Required Qualifications

• 8–10+ years of progressive experience in machine learning, advanced analytics, statistics, decision science or related disciplines, with a substantial record of production impact.
• 4+ years of experience leading, mentoring or managing high-performing data science teams is highly desirable.
• Recognised depth in several advanced areas such as causal inference, fraud detection, risk benchmarking, forecasting, survival methods, optimisation, personalisation, Bayesian modelling, uncertainty quantification or responsible AI.
• Proven record of deploying, monitoring and improving production-grade ML models that create measurable business, operational or health impact.
• Proven ability to set technical strategy and architecture across multiple products, markets or high-impact programmes.
• Demonstrated leadership of complex, cross-functional initiatives, with the ability to influence senior leaders without relying solely on formal authority.
• Expert proficiency in modern data science ecosystems, including Python, SQL, PySpark, Databricks, MLflow, PyTorch, distributed processing and cloud-based ML platforms.
• Experience defining governance, adoption metrics and value measures for AI-assisted development and agentic process automation.
• Exceptional communication, technical judgement and talent-development capability.
• Demonstrated ability to work in a fast-paced, demanding environment while managing stakeholder expectations, priorities and timelines.

Preferred Qualifications

• Experience in healthcare, health insurance, payer/provider analytics, population health, claims, care management, clinical operations or health data platforms.
• Experience working with healthcare privacy, security, governance and responsible AI requirements in regulated environments.
• Hands-on experience with Azure, Databricks or equivalent cloud and data platforms.
• Strong knowledge of CI/CD, MLOps, LLMOps, monitoring, governance and scalable deployment patterns.
• Experience with healthcare risk adjustment, utilisation, cost, provider performance, population health or member-level predictive modelling.
• Extensive experience with feature stores, model registries, experiment tracking, responsible AI practices and production model monitoring.

What Good Looks Like

• Is sought out as the organisation’s technical authority for high-stakes ML and analytical decisions.
• Shapes portfolio strategy and creates reusable capabilities with impact beyond a single project, market or product.
• Balances innovation with evidence, governance, cost, scalability and operational reality.
• Builds sustainable organisational leverage through standards, platforms, agents and talent development.
• Contributes credible external thought leadership while protecting proprietary knowledge.