Data Scientist

Career Category Engineering Job Description ABOUT AMGEN Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, making people’s lives easier, fuller, and longer. We discover, develop, manufacture, and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting edge of innovation, using technology and human genetic data to push beyond what’s known today. ABOUT THE ROLE The Data Scientist – Agentic AI & Scientific Systems is a senior technical contributor responsible for designing, building, and integrating AI capabilities that accelerate scientific discovery across domains such as protein engineering, structure prediction, disease biology, and target identification. This role focuses on developing agentic AI systems and scientific AI workflows that combine foundation models, domain-specific models, knowledge sources, and computational tools into reusable solutions that support scientific decision-making. The engineer works closely with scientific domain leads to translate research needs into scalable AI solutions and reusable capabilities. This role serves as a bridge between scientific innovation and enterprise AI platforms, helping establish a foundation for next-generation AI-assisted scientific workflows. Core Responsibilities Agentic AI Systems Development Design and implement agent-based systems that support complex scientific workflows. Develop capabilities including: Tool calling and tool orchestration Multi-step reasoning workflows Retrieval-augmented generation (RAG) Knowledge-grounded AI systems Human-in-the-loop decision workflows Multi-agent collaboration patterns Build reusable components for: Agent orchestration Context management Memory and state handling Workflow planning and execution Scientific tool integration Evaluate emerging agent frameworks and contribute to standards and best practices across projects. Scientific AI & Model Integration Integrate foundation models and scientific AI models into end-to-end workflows. Examples may include: Protein language models Structure prediction models Biological foundation models Knowledge graph-based systems Predictive machine learning models Develop reusable APIs, services, and interfaces that allow AI agents and applications to consume scientific models and computational tools. Collaborate with scientific domain experts to identify appropriate modeling approaches and evaluate solution effectiveness. Knowledge Systems & Retrieval Design and implement knowledge-driven AI systems that connect LLMs and agents with enterprise and scientific data. Develop solutions utilizing : Retrieval-augmented generation (RAG) Vector databases Knowledge graphs Graph-RAG architectures Scientific literature and domain knowledge repositories Ensure AI systems leverage authoritative knowledge sources and support traceability and explainability. AI Workflow Engineering Develop end-to-end workflows that combine: Data ingestion and preparation Knowledge retrieval Model inference Agent orchestration Scientific analysis Create reusable workflow patterns that can be applied across multiple scientific domains and projects. Contribute to architectural decisions regarding workflow design, model integration, and AI system composition. Evaluation & Responsible AI Develop evaluation frameworks for AI systems, agents, and workflows. Establish approaches for measuring: Accuracy Reliability Scientific relevance Hallucination rates Workflow effectiveness User adoption and impact <d