Senior Data Scientist - Computational Biology

Role Overview: As a Senior Data Scientist specializing in Computational Biology at Amgen, you will play a crucial role in designing, implementing, and advancing advanced analytical and AI-driven frameworks. Your primary responsibility will be to enable translational and reverse-translational insights from clinical trial data across Amgen's global development portfolio. This position requires expertise in computational biology, advanced statistics, and modern machine learning with a focus on predictive and prognostic biomarker modeling, multi-omic data integration, and next-generation AI-enabled analytical platforms. Key Responsibilities: - Design and implement predictive and prognostic biomarker models using clinical trial and biomarker data, covering response, resistance, disease stratification, and adverse event endpoints. - Develop and apply multi-omic integration frameworks to jointly analyze genomics, transcriptomics, proteomics, epigenomics, imaging, and clinical covariates. - Apply advanced statistical methodologies relevant to clinical development such as longitudinal and mixed-effects models, survival analysis, missing data imputation strategies, and model interpretability. - Contribute to study-level and cross-program analyses to inform mechanism of action, patient selection strategies, and development decisions. - Build and evaluate machine learning, deep learning, and causal inference models applied to biological and clinical data, ensuring clear understanding of model assumptions, limitations, and validation in regulated environments. - Develop or contribute to AI-enabled analytical systems, including foundation and large language model-based approaches, generative models, and agentic AI systems to support analysis, decision-making, or platform capabilities. - Collaborate with biomarker scientists, clinicians, biostatisticians, and data engineers across global teams, translating complex analytical results effectively through technical documentation, presentations, and cross-functional forums. Qualification Required: - 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. - Master's degree in a quantitative discipline with 3-5 years of relevant experience and 2-3 years of experience in an industry setting. Additional Details: The successful candidate for this role at Amgen should have a strong background in quantitative and computational depth, translational and clinical relevance, AI, innovation, and platform mindset, as well as professional and global operating skills. Preferred qualifications include hands-on experience developing statistical or machine learning models, expertise in Python and R, familiarity with modern ML/DL libraries, understanding of drug development and clinical trial data, and experience working with global teams and stakeholders. (Note: The above job description is tailored as per the provided job details and requirements at Amgen for the role of Senior Data Scientist in Computational Biology.) Role Overview: As a Senior Data Scientist specializing in Computational Biology at Amgen, you will play a crucial role in designing, implementing, and advancing advanced analytical and AI-driven frameworks. Your primary responsibility will be to enable translational and reverse-translational insights from clinical trial data across Amgen's global development portfolio. This position requires expertise in computational biology, advanced statistics, and modern machine learning with a focus on predictive and prognostic biomarker modeling, multi-omic data integration, and next-generation AI-enabled analytical platforms. Key Responsibilities: - Design and implement predictive and prognostic biomarker models using clinical trial and biomarker data, covering response, resistance, disease stratification, and adverse event endpoints. - Develop and apply multi-omic integration frameworks to jointly analyze genomics, transcriptomics, proteomics, epigenomics, imaging, and clinical covariates. - Apply advanced statistical methodologies relevant to clinical development such as longitudinal and mixed-effects models, survival analysis, missing data imputation strategies, and model interpretability. - Contribute to study-level and cross-program analyses to inform mechanism of action, patient selection strategies, and development decisions. - Build and evaluate machine learning, deep learning, and causal inference models applied to biological and clinical data, ensuring clear understanding of model assumptions, limitations, and validation in regulated environments. - Develop or contribute to AI-enabled analytical systems, including foundation and large language model-based approaches, generative models, and agentic AI systems to support analysis, decision-making, or platform capabilities. - Collaborate with biomarker scientists, clinicians