A.       PRINCIPAL RESPONSIBILITIES

• Design, develop, and implement advanced analytics and machine learning models, including LLM-powered and agentic AI solutions, to support clinical decision-making and care pathway optimisation.
• Build, maintain, and optimise automated data pipelines and ETL processes.
• Clean, preprocess, validate, and analyse large healthcare datasets to ensure accuracy, consistency, and completeness.
• Apply advanced statistical methods and data mining techniques to identify patterns, trends, and insights in healthcare data.
• Develop predictive models and analytical tools to support population health and primary care initiatives.
• Collaborate closely with clinical and operational teams to integrate AI and analytics solutions into real-world care workflows.
• Create interactive dashboards and analytical reports with a strong focus on usability and clarity for non-technical stakeholders.

B.       SECONDARY RESPONSIBILITIES

The Senior Research Analyst may also be part of a study team with the following responsibilities:

• Monitor data integrity over time and proactively identify, investigate, and resolve data quality issues or anomalies.
• Develop and implement systematic data quality checks and validation frameworks.
• Prepare clear, concise reports and presentations to communicate findings, insights, and recommendations to stakeholders.
• Support ongoing evaluation, validation, and refinement of analytics and AI solutions in real-world settings.

Undertake additional duties and responsibilities as assigned by the Reporting Officer.

Requirements:

• Master’s or PhD degree in a quantitative discipline such as Computer Science, Mathematics,Data Analytics, Data Science, Statistics, or a related field.
• Formal training in data science, analytics,machine learning, or artificial intelligence is required.
•  3–5 years of relevant experience in a data science, analytics, orresearch-focused role.
• Demonstrated experience working with large, complex datasets,preferably in healthcare or population health settings.
• Experience designing, developing, and deploying machine learningor advanced analytics solutions.
• Prior exposure to healthcare data, clinicalworkflows, or real-world health system implementation is an advantage.