Senior Scientist - Computational Systems & Predictive Biology

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Senior Scientist - Computational Systems & Predictive Biology

We are seeking a highly qualified and motivated Senior Scientist with a strong background in computational biology and machine learning 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 scaling emerging computational technologies to drive the next generation of target and therapeutic discovery.

This role focuses on developing scientific platforms and predictive systems that enable scalable, reproducible, and therapeutic area‑agnostic application of computational biology across target discovery and validation. You will develop and operationalize computational models into reusable systems, define canonical data and analytical representations across modalities, and build predictive frameworks that enable in silico interrogation of biological systems where experimental data are limited or infeasible.

The successful candidate will combine strong analytical rigor with deep expertise in computational biology and machine learning, including predictive and generative modeling approaches, and a strong understanding of biological systems. A demonstrated ability to translate complex data and computational models into scalable computational systems, tools, and workflows that enable in silico interrogation of biological systems and support target prioritization, credentialing, and therapeutic decision‑making is essential.

• Develop and implement computational systems that standardize and operationalize data, models, and analytical methods into reusable, scalable frameworks supporting target discovery, validation, and prioritization across therapeutic areas.
• Define and build canonical data representations and analytical abstractions across multimodal datasets, including perturbation biology, surfaceome features, and variant‑to‑gene‑to‑function, enabling consistent and TA‑agnostic application of computational methods.
• Design and develop predictive systems to model molecular and cellular profiles, enabling in silico interrogation of biological systems where experimental data are limited or infeasible.
• Translate computational models and analytical approaches into user‑facing tools, platforms, and interfaces, including internal applications and agentic systems, improving accessibility and impact across discovery workflows.
• Enable experimental scientists by accelerating data analysis, guiding experimental design, and generating actionable hypotheses for modality‑driven target discovery and validation, particularly in contexts where experiments are costly, limited, or infeasible.
• Collaborate closely with computational and experimental scientists to align system design with biological questions, and partner with data engineering and technology teams to enable robust deployment…