Data Analyst (DMODT) - Real-World Evidence

About the role

SingHealth is hiring a Data Analyst (DMODT) to join the Data Specialist team supporting the National Cancer Centre Singapore (NCCS). The role manages, curates and analyzes clinical data to generate actionable insights, enhances existing databases, ensures data integrity and integrates outputs with ongoing and future initiatives. It involves engaging clinicians, researchers, industry and regulatory partners to strengthen real-world evidence in oncology.

Key responsibilities

• Curate, clean, standardize and integrate clinical, genomic, pathology, laboratory, registry and trial datasets, ensuring quality, consistency, completeness and compliance with data governance requirements.
• Manage and maintain clinical and genomics databases on AWS and institutional platforms, monitoring performance, security, access controls, backups and costs.
• Develop and maintain genomic analysis pipelines on AWS, automate data processing and quality control, build federated data-sharing pipelines and ensure interoperability with national and international standards.
• Perform statistical analyses, data mining and visualization, identify trends and biomarkers, and produce dashboards, reports and outputs for research, grants and publications.
• Support clinical investigators with study design, data extraction and result interpretation, and contribute to grant proposals, manuscripts, abstracts and conference presentations.

Required profile

• Bachelor's or Master's degree in Biomedical Sciences, Public Health, Biostatistics, Bioinformatics, Data Science or a related discipline.
• Experience with clinical or healthcare datasets such as electronic health records, registries, clinical trials and real-world evidence.
• Familiarity with medical terminology and coding standards (ICD, SNOMED, LOINC).
• Strong analytical, problem-solving and data interpretation skills, and the ability to explain complex findings to technical and clinical audiences.
• Attention to detail, collaborative mindset, adaptability and interest in translational research and multi-stakeholder collaboration.

Required skills

• Advantageous: familiarity with the OMOP Common Data Model (CDM), data mapping and terminology standardization.
• Preferred: experience in medical coding, terminology mapping or healthcare data standardization.