Senior Data Scientist Surfaceome Biology

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.

By integrating diverse experimental and computational approaches, this role will build reusable evidence and data-integration capabilities that connect high-value external biomedical datasets and knowledge with internal data, analytical workflows, and AI/ML models. Our goal is to create a richer, more reliable evidence base that strengthens target discovery, target validation, biomarker identification, and understanding of disease biology. These insights are pivotal for advancing the next generation of targeted and multi specific drug modalities.

In this role, you will lead technically complex projects spanning the evaluation and qualification of external evidence sources, harmonization and integration of diverse biological datasets, development of AI/ML-enabled analytical methods, and delivery of robust data and model products. You will work with global computational, experimental, and translational colleagues to translate complex data into testable hypotheses and discovery decisions. The successful candidate will possess strong analytical aptitude, deep technical expertise in computational sciences and AI/ML, and a solid understanding of molecular and cellular biology, proven by a track record of innovative and collaborative research.

RESPONSIBILITIES:

• Identify, assess, and qualify high-value external biomedical datasets and evidence sources, including omics, functional-genomics, imaging, clinical and translational data, biomedical knowledge bases, and literature-derived evidence.
• Design scalable data pipelines and common data models for the acquisition, curation, harmonization, quality control, metadata standardization, and integration of external and internal data assets.
• Develop and apply advanced computational, statistical, and AI/ML methods - including multimodal learning, representation learning, foundation models, knowledge-graph approaches, and AI-assisted analytical workflows - to synthesize diverse evidence and generate insights for target discovery, validation, and biomarker development.
• Build and operate reproducible data and model products using DataOps and MLOps practices, including workflow orchestration, data and model versioning, automated testing and validation, deployment, monitoring, and continuous improvement.
• Partner with discovery, experimental, and translational teams to define high-value use cases, interpret integrated evidence, guide follow-up analyses or studies, and ensure that computational outputs inform biological and therapeutic decisions.
  
  

Win

What We Expect Of You

We are all different, yet we all use our unique contributions to serve patients. The dynamic professional we seek is a scientist with these qualifications.

Basic Qualifications:

PhD in computational biology, bioinformatics, computer science, data science, or a related quantitative field; preferably with industry experience

Or

Master’s degree Or Bachelor’s degree and 8 -12 years of directly related experience

Preferred Qualifications:

• Strong foundation in computational biology, bioinformatics, data science, or a related quantitative discipline, with experience working with complex biomedical data.
• Experience evaluating, qualifying, and integrating diverse external biomedical datasets from public, commercial, academic, or partner sources, with attention to study design, metadata, data quality, provenance, and fitness for purpose.
• Experience with multi-omics and multimodal data integration, including harmonization across studies, technologies, and biological contexts.
• Demonstrated experience developing or applying machine-learning and AI methods for biological systems, such as foundation models, representation learning, generative models, knowledge-graph methods, or causal and mechanistic modeling.
• Proven ability to translate computational analyses into actionable biological insights, target-validation evidence, biomarker hypotheses, or therapeutic decisions.
• Strong programming skills in Python, R, or similar languages, with experience developing reproducible, well-documented analytical workflows and scientific software.
• Experience with cloud-scale data processing, workflow orchestration, and software-engineering practices such as version control, continuous integration, and automated testing.
• Practical experience with MLOps, including experiment tracking, data and model versioning, validation, deployment, monitoring, and maintenance of production-quality analytical or machine-learning systems.
• Familiarity with agentic AI, LLM-enabled scientific workflows, or digital innovation approaches, and sound judgment about their reliable use in a scientific setting.
• Excellent communication and collaboration skills, with the ability to work across computational and experimental disciplines, present complex findings clearly, and contribute effectively to global, cross-functional teams.
• Strong interpersonal and collaborative skills with demonstrated ability to thrive in cross-functional teams and effectively present results to diverse audiences.
• Creative, open-minded, and passionate about research, with a proven record of innovative algorithm and model development demonstrated through impactful publications, patents, or widely adopted tools.

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