Senior Data Scientist, Computational Biology

Career Category Clinical Development Job Description Role Summary The AIN-based Computational Biology Senior Data Scientist will design, implement, and advance advanced analytical and AI-driven frameworks to enable translational and reverse-translational insights from clinical trial data across Amgen’s global development portfolio. This role sits at the intersection of computational biology, advanced statistics, and modern machine learning, with a strong emphasis on predictive and prognostic biomarker modeling , multi-omic data integration , and next-generation AI-enabled analytical platforms . The successful candidate will contribute intellectually and technically to Amgen’s precision medicine strategy by developing rigorous, scalable, and scientifically interpretable models that link molecular, cellular, and clinical phenotypes to disease stratification, efficacy, safety, and adverse event outcomes. This role requires demonstrated depth—not familiarity—in applied modeling, multi-omic analytics, and AI systems, with evidence of impact through peer-reviewed publications, production-grade codebases, or verifiable industry experience.

Advanced Modeling & Translational Analytics Design and implement predictive and prognostic biomarker models using clinical trial and biomarker data, including response, resistance, disease stratification, and adverse event endpoints. Develop and apply multi-omic integration frameworks (e.g., factor models, MOFA-style latent variable approaches, matrix factorization, graph-based methods) to jointly analyze genomics, transcriptomics, proteomics, epigenomics, imaging, and clinical covariates. Apply advanced statistical methodologies relevant to clinical development, including longitudinal and mixed-effects models, survival analysis, missing data and imputation strategies, confounder adjustment, and model interpretability. Contribute to study-level and cross-program analyses that inform mechanism of action, patient selection strategies, and development decisions. Machine Learning, AI & Emerging Capabilities Build and evaluate machine learning, deep learning and causal inference models applied to biological and clinical data, with a clear understanding of model assumptions, limitations, and validation in regulated environments. Develop or meaningfully contribute to AI-enabled analytical systems, including: Foundation and large language model–based approaches (e.g., GPT-class models) for scientific workflows Generative models for representation learning, hypothesis generation, or simulation Agentic AI systems (assistive, conversational, automated, predictive, or sentinel) to support analysis, decision-making, or platform capabilities Partner with platform and engineering teams to ensure analytical methods are reproducible, scalable, and production-ready. Scientific Rigor, Collaboration & Communication Translate biological and clinical questions into well-defined analytical strategies and clearly articulate modeling choices, assumptions, and uncertainties. Collaborate closely with biomarker scientists, clinicians, biostatisticians, and data engineers across global teams and time zones. Communicate complex analytical results and their implications effectively through technical documentation, presentations, and cross-functional forums. Operate with scientific independence while proactively seeking alignment and clarification in a highly matrixed, global development environment.

Doctorate degree OR Master’s degree in Bioinformatics, Computational Biology, Statistics, Mathematics, Computer Science, Data Science, or a related quantitative discipline with 8 + years of relevant experience AND in a quantitative discipline with 3–5 years of relevant experience and 2-3 years of experience in an industry setting Preferred Qualifications Candidates should provide verifiable evidence for most of the following: Quantitative & Computational Depth Demonstrated,