Data Scientist (Healthcare)
Summary
Build and deploy LLM-based AI systems for healthcare, including clinical decision support and automated documentation, using Python, AWS, and MLOps pipelines.
Responsibilities
Lead the research, design, development, and deployment of LLM-based and agentic AI solutions to address clinical and operational challenges.
Build and maintain automated data pipelines, prompt engineering workflows, and retrieval-augmented generation (RAG) systems for AI applications.
Develop LLM-powered applications for clinical decision support, care pathway optimisation, automated documentation, and clinical research initiatives.
Apply NLP techniques to clean, preprocess, validate, and analyse healthcare data, ensuring high data quality and extracting actionable insights.
Collaborate with cross-functional teams to integrate AI solutions into existing clinical platforms and data infrastructure.
Monitor application performance, develop explainability frameworks, create reports and visualisations, and communicate findings to technical and non-technical stakeholders.
Ensure compliance with healthcare data governance, security, privacy regulations (e.g., PDPA, HBRA), and ethical AI implementation, including data anonymisation and access management.
Requirements
Minimum 2 years of experience in data science or a related field, preferably within a healthcare environment.
Proven experience developing and deploying production-grade LLM and agentic AI applications, supported by a GitHub portfolio or documented project case studies.
Strong programming skills in Python, R, and/or SQL, with experience integrating AI/ML models, LLM APIs, and cloud platforms (especially AWS).
Solid understanding of CI/CD, MLOps, and DevSecOps practices for AI application development and deployment.
Strong analytical, problem-solving, and communication skills, with the ability to translate clinical and operational needs into practical AI solutions.
GMP Recruitment Services (S) Pte Ltd | EA Licence: 09C3051 | VO UYEN AI LINH | Registration No: R22109232