Data Scientist
Career Category
EngineeringJob 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 establishthe biotechnology industry more than 40 years ago and remainson the cutting edgeof innovation, using technology and human genetic data to push beyond what’sknown 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 leadsto translateresearch needs into scalable AI solutions and reusable capabilities. This role serves as a bridge between scientific innovation and enterprise AI platforms, helping establisha 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 standardsand 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 modelingapproaches 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 leverageauthoritative 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 regardingworkflow 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
Support responsible AI practices including transparency, traceability, and governance requirements.
Collaboration & Scientific Partnership
Partner closely with:
AI domain leads
Scientists and researchers
Data engineering teams
Platform engineering teams
Enterprise AI platform teams
Translate scientific requirements into technical solutions and provide guidance on AI capabilities, limitations, and implementation approaches.
Contribute to technical design reviews and mentor junior team members where appropriate.
Core Competencies
Strong engineering background in AI and machine learning systems.
Hands-on experience with:
Large Language Models (LLMs)
Agent frameworks (LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or similar)
Retrieval-Augmented Generation (RAG)
Vector databases
API-driven architectures
Python-based AI and ML ecosystems
Understanding of:
Machine learning lifecycle and evaluation
Scientific computing workflows
Distributed systems and scalable architectures
Knowledge graph concepts and graph-based AI approaches
Ability to operateeffectively in highly collaborative, cross-functional scientific environments.
Core Success Measures
Delivery of reusable AI capabilities and agentic workflows
Adoption of AI solutions by scientific teams
Quality and reliability of deployed AI systems
Reduction of manual effort through workflow automation
Reusability of components across multiple scientific domains
Effective collaboration with scientific and engineering stakeholders
Key Relationships
Works closely with:
Senior Scientific AI Leads
Scientists and domain experts
Data Engineering teams
Enterprise AI Platform teams
Infrastructure and production engineering organizations
Decision Authority
Makesimplementation decisions regarding:
Agent architectures
Workflow composition
Knowledge retrieval strategies
Model integration approaches
Evaluation methodologies
Influences broader architectural direction through technical expertiseand collaboration with senior technical leaders.
Qualifications
Basic Qualifications
BS or MS in Computer Science, Engineering, Computational Biology, Bioinformatics, or related field
Strong hands-on experience developing AI and machine learning solutions
Expertisein Python and modern AI/ML development frameworks
Experience designing and implementing production-quality software systems
Preferred Qualifications
Experience with LLMs, agenticAI systems, and workflow orchestration
Experience with RAG, vector databases, and knowledge-driven AI architectures
Experience integrating scientific or domain-specific AI models
Familiarity with biological, biomedical, or life sciences data
Experience with cloud AI platforms (AWS Bedrock, SageMaker, Azure AI, or equivalent)
Familiarity with knowledge graphs, Graph-RAG, or scientific knowledge systems
Experience working closely with researchers and domain experts.
Preferred Experience:
Bachelor'swith 5-9 years of experience.
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