Research Fellow/ Senior Research Fellow (Ref: NHCS/RF/JY)

We are seeking experienced and highly motivated individuals to join ourgrowing research team in data science and artificial intelligence (AI).

The successful candidates will work closely with the Principal Investigator (PI) to drive advanced analytics and AI initiatives across large‑scale, multimodal healthcare datasets. Your work will span statistical modelling, machine learning, deep learning and methodology development, with strong opportunities to lead analyses, co‑develop study protocols, and contribute to high‑impact scientific publications.

This role offers meaningful career development, visibility in cross‑institution collaborations, and the chance to build a strong publication portfolio - ideal for candidates seeking to advance their scientific career and domain expertise. You will thrive in a supportive, multidisciplinary environment with access to real‑world clinical datasets, cross‑functional experts, and opportunities to participate in national‑level precision medicine and population health initiatives.

Job Requirements

1. PhD in a quantitative discipline (e.g.,Computer Science/Engineering, Statistics/Biostatistics, Bioinformatics, Mathematics, Business Analytics, Information Systems, Operations Research, Physics) or equivalent.

2. At least one year's technical experience in analytics or data science, with prior exposure to healthcare data and large-scale, real-world health datasets (e.g., registries, claims, omics, wearable data).

3. Experience in managing metadata repositories, developing data dictionaries and catalogues, and maintaining semantic documentation to support data governance and standards development.

4. Proficiency in Python, R, and SQL, with experience in big-data frameworks (e.g., Spark/Scala, PySpark).

5. Strong expertise in machine learning and model development experience in deep learning and advanced analytical methods (e.g. causal inference, survival modelling, latent class analysis, omics analytics) preferred.

6. Strong understanding of the OMOP Common Data Model, including CDM tables, relationships, and vocabulary conventions, with familiarity with standardised vocabularies such as SNOMED, LOINC, RxNorm, ICD10-AM, CPT, and NDC.