MOHT envisions a transformed health system that is patient-centric, data-driven and digitally enabled to better empower health, prevent disease and provide excellent value-based care. To realise this vision, MOHT's mission is to design and implement innovative solutions essential for the desired health system transformation.
MOHT is seeking a strategic and technically strong Data Scientist (Health Analytics /AI Scientist) to lead applied AI/ML research and deployment initiatives that support national healthcare transformation efforts.
In this role, you will translate clinical, policy and operational objectives into structured research programmes and scalable digital health solutions. You will drive the end-to-end lifecycle of AI-enabled initiatives - from conceptualisation and experimental design to pilot and real-world deployment - ensuring solutions are clinically sound, operationally scalable, and aligned with policy priorities.
This is a high-impact role suited for candidates who are passionate about building production-grade AI systems within a regulated healthcare environment.
JOB RESPONSIBILITIES
- Lead R&D strategy and technical roadmaps aligned with national healthcare transformation priorities. Define research objectives, milestones, governance frameworks, and performance metrics for AI-enabled health analytics programmes.
- Design and oversee rigorous experimental frameworks, including cohort definition, outcome modelling, benchmarking,validation, calibration, and bias mitigation. Evaluate outputs andindependently determine methodological refinements to ensure clinical validity, operational feasibility, and policy alignment.
- Assess technical feasibility and authorise progression from prototype to pilot and production. Establish and enforce standards for model evaluation, responsible AI deployment, data governance, recalibration cycles, risk thresholds, and performance monitoring within regulated healthcare environments.
- Lead development of disease progression, screening prioritisation, service forecasting, and healthcare financing models, incorporating advanced feature engineering and architecture optimisation. Direct AI-driven patient engagement systems, including LLM orchestration and adaptive optimisation mechanisms.
- Guide digital phenotyping and wearable-based relapse prediction initiatives. Provide technical mentorship and presentresearch findings and policy recommendations to senior leadership and stakeholders to inform strategic decisions.
- Provides technical direction and oversight to (1) 2 externalfull-time vendor staff (product and engineering functions) and (2) Cross-functional working groups including clinicians, policy analysts, data engineers, and implementation partners.
- Serves as technical lead for multidisciplinary research programs spanning analytics, digital product development, and system deployment.
- Exercises decision-making authority on research design, model deployment readiness, technical architecture, and evaluation standards.
JOB RESPONSIBILITIES
- PhD or Master's degree in Data Science, ComputerScience, Engineering, Physics, or related quantitative discipline
- Strong experience in applied AI/ML research anddeployment in healthcare or large-scale data environments
- Expertise in supervised learning, time-series modelling, predictive analytics, risk stratification, model calibration, and evaluation design
- Experience with LLM orchestration, adaptive learning systems, and AI-driven digital health tools
- Experience in production model deployment, MLOps, and scalable AI pipelines
- Demonstrated ability to lead cross-functional initiatives and manage external collaborators
- Strong written and verbal communication skills, including executive-level reporting
