Scientist – Research Computational Biology (ARIA)

Career Category Research Job Description The ARIA Computational Biology team is seeking a talented and creative computational biologist to further our mission of serving patients by advancing and replenishing the Amgen therapeutic pipeline. In this role you will apply your expertise in data science and disease biology to accelerate the identification, prioritization, and validation of transformative therapeutic targets in a dynamic cross-functional research environment. Focus areas include: Extracting biological insight from complex multi-modal omics and screening data to characterize disease endotypes and mechanisms, identify novel targets, and test therapeutic hypotheses. Developing and leveraging methods/platforms to (1) guide prioritization of targets across diverse diseases and therapeutic modalities and (2) deliver foundational target insights, including characterization of expression biodistribution and isoform complexity. Developing advanced capabilities for discovery of novel disease-enriched isoforms using long-read transcriptomics, proteomics, and screening technologies. Partnering with our data science and information systems teams to incorporate tools and analyses developed within ARIA Computational Biology into integrated R&D platforms. Basic Qualifications: Ph.D. in computational biology, bioinformatics, data science, or a related discipline OR Master’s degree with 3+ years of relevant research experience. Strong programming skills (Python, R, Linux/Unix), familiarity with cloud computing environments, HPC, and collaborative coding practices (e.g., Git). Track record of designing and executing holistic computational strategies to address challenging research questions. Proven expertise in the analysis and interpretation of single cell omics data. Excellent presentation and communication skills to convey complex findings to diverse audiences. Self-starter with a collaborative mindset and a drive for continuous growth. Preferred Qualifications: Solid immunology background, including application to the interpretation of single-cell oncology and/or inflammatory disease data. Strong understanding of RNA and protein isoform complexity, including underlying biology and computational approaches for characterization using long-read transcriptomics. Experience leveraging and fine-tuning transcriptional foundation models and biomedical knowledge graphs to further research goals. Familiarity with public data resources (e.g. DepMap, Human Cell Atlas, TCGA, GTEx, and Tahoe-100M) frequently used to augment analyses of internally generated data. Experience developing and deploying tools and pipelines to endpoints such as interactive portals (e.g. RShiny apps), workflow management systems (e.g Nextflow), and agentic frameworks. .