Data Scientist (DMSMC)

We are seeking a highly motivated and talented individual with a passion for oncology, genomics, and data science research to join the Precision Radiotherapeutics and Oncology Programme and the Data and Computational Science Core at the National Cancer Centre Singapore. The successful candidate will work closely with the Principal Investigator (PI) and the existing research and clinical teams. They will be expected to contribute actively to our core multi-omics research and big-data analysis infrastructure, which focuses on integrating electronic medical records, next-generation sequencing (NGS), radiological imaging, and other multimodal data types to develop biomarkers that predict clinical responses in patients with cancer.

Specifically, the Data Scientist will be expected to: - Optimise genomic, transcriptomic, and radiomic data-processing and statistical-analysis pipelines. - Perform feature engineering for statistical modelling, machine learning, and deep learning. - Conduct survival analyses and other relevant computational analyses to better understand cancer progression and treatment resistance across multiple cancer types. - Contribute to and guide research discussions with scientists and clinical collaborators.

The position offers ample opportunities for interdepartmental and cross-institutional collaboration with oncologists, pathologists, and scientists. For more information, please visit the laboratory website: www.chualabnccs.com.

Requirements: - A postgraduate degree, at least at the master’s level, in bioinformatics, quantitative or computational biology, data science, computer science, or a related field. - Highly motivated, organised, meticulous, and committed to maintaining high-quality standards. - For PhD holders, at least two years of experience in analysing multi-omics data, such as whole-exome sequencing and RNA-sequencing data, and clinical data using statistical methods, classical machine learning, deep learning, and language models. - Prior experience in data curation, cleaning, organisation, and management. - Proficiency in Python, R, or another relevant programming language.

(Good to have) - Demonstrable data analysis skills with a portfolio on relevant datasets (e.g. TCGA, Kaggle). - Familiarity with command line shells (e.g. bash, zsh). - Prior experience working with high-performance computing clusters and job schedulers. - Knowledge and experience in biostatistical analyses of clinical datasets. - Knowledge and experience in multi-omics analyses (e.g. analyses of WES, RNASeq data). - Able to work independently under pressure as well as in a team. - Strong organizational, interpersonal and presentation skills. - Responsible, analytical and self-confident with a mature personality. - Keen interest to solve clinical problems.