Senior Scientist - Computational Systems & Predictive Biology

Amgen•South San Francisco, CA

About The Position

Join Amgen’s Mission of Serving Patients At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do. Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives. Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career. Senior Scientist - Computational Systems & Predictive Biology What you will do Let’s do this. Let’s change the world. 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.

Requirements

PhD in computational biology, bioinformatics, statistics, computer science, data science, or a related quantitative discipline [and relevant post-doc where applicable]
Master’s degree and 3 years of directly related experience
Bachelor’s degree and 5 years of directly related experience

Nice To Haves

Strong background in computational biology methods, including statistical modeling and AI/ML, with demonstrated ability to model biological systems.
Expertise in developing machine learning approaches for biological data, including predictive modeling and, ideally, experience with generative or representation learning methods.
Experience working with multimodal biological datasets, including perturbation screens , transcriptomics, single-cell and spatial omics, proteomics, and genetic or epigenomic data, with the ability to integrate these into unified computational frameworks.
Demonstrated ability to develop computational abstractions and canonical representations that enable consistent, reusable analysis across datasets, modalities, or disease contexts.
Experience building predictive or generative models that infer molecular and cellular responses, particularly in contexts where experimental data are limited or incomplete.
Proven ability to translate computational models into scalable and reusable tools, frameworks, or systems that are adopted by scientists and support real-world discovery workflows.
Strong programming skills in Python, R, Linux/Unix, or similar languages, with experience developing modular, maintainable, and well-structured computational codebases.
Familiarity with workflow management systems (e.g., Nextflow) and AWS cloud infrastructure a plus.
Experience working in cross-functional environments, with excellent communication and collaboration skills, and a demonstrated ability to translate complex computational results into biologically meaningful insights.
Track record of computational innovation demonstrated through impactful publications, patents, or contributions to computational methods, modeling frameworks, or scientific software.

Responsibilities

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 and scaling while maintaining ownership of scientific modeling, data abstractions, and analytical design.
Drive innovation by identifying gaps in computational workflows and integrating emerging approaches, including generative modeling and representation learning, into scalable systems that enhance target validation and therapeutic hypothesis generation.

Benefits

comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts
discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
Stock-based long-term incentives
Award-winning time-off plans
Flexible work models where possible

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