Data Scientist (CLDE under Precision Health), BII

The role is ideal for a dedicated Data Scientist with up to 5 years of experience who is passionate about applying clinical AI and advanced analytics to improve patient outcomes, optimize healthcare delivery, and support evidence-based decision-making. The Data Scientist will work with diverse datasets including electronic health records (EHR), clinical coding standards, genomics, and public health registries to develop data-driven solutions that impact population health at scale in Singapore.

Key Responsibilities

Analyze and integrate large-scale healthcare datasets (EHR, claims, genomics, registries, public health surveillance).

Develop, validate, and deploy predictive and prescriptive models to support clinical decision-making and public health interventions.

Apply machine learning and natural language processing (NLP) to clinical narratives, biomedical text, and unstructured health data.

Collaborate with clinicians and public health experts to identify high-impact problems and translate them into technical solutions.

Conduct data-driven evaluations of health interventions, screening programs, and risk stratification tools.

Ensure data quality, security, and compliance with ethical AI principles.

Build visualizations and decision-support tools for clinical and policy stakeholders.

Contribute to publications and presentations on applied clinical AI and health data science.

Required Qualifications

Bachelor?s or Master?s degree in Data Science, Computer Science, or related discipline.

2?5 years of experience in data science / applied machine learning ? ideally in a health setting.

Excellent programming skills in Python

Proficiency with machine learning frameworks (scikit-learn, TensorFlow, PyTorch).

Experience with healthcare data standards and ontologies (e.g., SNOMED CT, ICD, LOINC, HL7 / FHIR).

Ability to communicate results clearly to clinical and non-technical audiences.

Preferred Skills

Experience with and / or interest in novel deep learning architectures ? e.g., fusion or energy-based models and fine-tuning large language models.

Exposure to EHR, real-world evidence or clinical trial data.

Exposure to time-series modeling and risk prediction models.

Familiarity with cloud platforms (AWS, GCP, Azure) and MLOps best practices.

Prior experience in multi-disciplinary teams involving clinicians, researchers, and policymakers