Job Description: We are seeking a highly motivated and collaborative Bioinformatics Scientist to support oncology drug discovery programs through advanced computational analysis and biological interpretation of large-scale omics datasets. The successful candidate will partner closely with discovery teams to derive biological insights from NGS and multi-omics data, contribute to experimental design, and enable data-driven decision-making across early- and late-stage programs. Responsibilities: Support oncology research by analysing high-throughput omics data, including RNAseq, WES, and other NGS platforms. Query public and internal cancer genomics resources to assess mutation/expression distributions and clinical relevance. Integrate and interpret internal datasets (e.g., transcriptomics, proteomics, single-cell, spatial omics) to uncover mechanisms of disease and therapeutic response. Communicate analytical findings and recommendations effectively to cross-functional teams with diverse scientific backgrounds. Collaborate with experimental and discovery scientists to optimize study designs and identify computational opportunities. Develop and maintain reproducible bioinformatics workflows and pipelines. Leverage cloud (AWS) and/or HPC infrastructure for scalable data analysis. Stay current with emerging technologies and data repositories to enhance data interpretation. Requirements: Ph.D. in Bioinformatics, Computational Biology, Genomics, Cancer Biology, or related field; OR. Master's degree with 1–3 years of relevant industry experience; OR. Bachelor's degree with 6–8 years of relevant experience. Demonstrated oncology background, preferably with exposure to immuno-oncology, immunology, or cancer genomics. Hands-on experience with NGS data analysis (e.g., RNAseq, WES), from raw data processing to downstream interpretation. Proficiency in R (including ggplot2); competence in Python is preferred. Experience working in Linux environments and using HPC clusters or cloud platforms (AWS). Strong data visualization and presentation skills. Excellent written and verbal communication skills. Strong team orientation and ability to work in cross-functional settings. Experience with single-cell RNA-seq, spatial omics, CRISPR screens, or proteomics data. Familiarity with public data repositories such as TCGA, GTEx, GEO, ENA/EBI, and GDC. Prior industry experience in pharmaceutical or biotech settings. Experience supporting drug discovery programs from early to late stages.