Senior Data Scientist – Perturbation Biology & AI

Career Category Research Job Description What you will do Let’s do this. Let’s change the world. We are seeking a highly qualified and motivated Senior Data Scientist with a strong background in computational biology to join the Bioinformatics Technologies team within Amgen’s Automation, Research Data Systems, Informatics, and AI (ARIA) organization. ARIA is a multidisciplinary group embedded within Amgen’s discovery engine, leveraging advancements in digital technologies for disease modeling and digital modality engineering to accelerate the pipeline from target inception through drug development. Within ARIA, Bioinformatics Technologies serves as an innovation hub for developing, deploying, and applying emerging digital technologies in computational biology to drive the next generation of therapeutic discovery. Perturbation screening is an essential approach for understanding how genetic and non-genetic changes impact cellular systems. By systematically probing gene function and network dependencies, these studies reveal disease mechanisms, identify novel therapeutic targets, and uncover biomarkers that can guide preclinical and translational research. This capability enables more confident target validation and accelerates the path from discovery to therapeutic hypothesis generation. In this role, you will develop and apply advanced computational and AI/ML methods to analyze perturbation screening data, strengthen data interpretability, and collaborate with experimental teams to ensure computational predictions translate into actionable insights for target and therapeutic discovery. The successful candidate will possess strong analytical aptitude, have a high degree of technical competency in computational sciences with a deep theoretical understanding of AI/ML and causal learning paired with an excellent understanding of molecular biology proven by a track record of innovative and collaborative research. RESPONSIBILITIES: Develop and apply advanced methodologies at the intersection of functional genomics and AI/ML analytics to identify and validate novel therapeutic targets, elucidate mechanisms of action, and discover preclinical biomarkers. Provide computational and data science support for high-throughput single-cell perturbation and phenotypic screening experiments (e.g., Perturb-seq) to map gene–phenotype relationships, uncover disease drivers, and prioritize hits for validation. Drive digital innovation by identifying gaps in analytical workflows and developing robust, reproducible, and FAIR solutions that improve data interpretability, strengthen readout confidence, and accelerate therapeutic hypothesis generation. Build and apply machine learning, deep learning, and generative AI approaches to simulate cellular responses to single or combinatorial genetic and non-genetic perturbations, delivering fit-for-purpose predictions that reduce experimental burden and accelerate therapeutic hypothesis generation.