Data Analyst (DMOAT)

The Data Specialist - Real-World Evidence (RWE) will support the research initiatives of the National Cancer Centre Singapore (NCCS) by managing, curating, and analyzing clinical data to generate actionable insights. The incumbent will be responsible for enhancing existing databases, ensuring data integrity, and integrating outputs with ongoing and future initiatives. By translating complex clinical data into meaningful outputs, the role will directly contribute to advancing patient care, research excellence, and policy impact. The position also offers opportunities to engage with multi-stakeholder partners, including clinicians, researchers, industry, and regulatory bodies, to strengthen the role of real-world evidence in oncology.

Your responsibilities will include:

Data Management & Integration:

- Curate, clean, and standardize clinical datasets from multiple sources (e.g., electronic health records, registries, laboratory/pathology systems, clinical trials).

- Incorporate new data into existing databases, ensuring accuracy, consistency, and completeness.

- Collaborate with IT, clinical, and research teams to improve database design and usability.

Data Analysis & Interpretation:

- Perform descriptive and statistical analyses of real-world evidence datasets.

- Translate clinical data into meaningful findings that inform research hypotheses, patient care, and outcomes evaluation.

- Identify data trends, patterns, and knowledge gaps for further investigation.

Integration with Existing and Planned Initiatives:

- Align data outputs with ongoing and future institutional, national, or international initiatives in real-world data and evidence.

- Ensure interoperability of datasets and harmonization with established standards and frameworks.

- Contribute data and insights to collaborative projects and multi-stakeholder platforms.

Requirements:

- Bachelor's or Master's degree in Biomedical Sciences, Public Health, Biostatistics, Bioinformatics, Data Science, or a related discipline.

- Experience working with clinical or healthcare datasets (e.g., EHRs, registries, clinical trials, observational studies).

- Familiarity with medical terminology and coding systems (e.g., ICD, SNOMED, LOINC).

- Strong analytical, problem-solving, and data interpretation skills.

- Ability to communicate complex findings effectively to both technical and clinical stakeholders.

- Strong attention to detail with commitment to data quality and accuracy.

- Collaborative mindset with ability to work across multidisciplinary teams.

- Adaptability and willingness to learn new tools, methodologies, and clinical domains.

- Motivation to contribute to translational research with potential impact on patient care.

- Interest in expanding into multi-stakeholder engagement, including collaborations with industry and regulatory agencies.