Principal Data Scientist - AI Context Architect; Semantic & Context Engineering

Position: Principal Data Scientist - AI Context Architect (Semantic & Context Engineering)
Location: California

Career Category

Information Systems

Job Description

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.

Principal Data Scientist What you will do

Let’s do this. Let’s change the world. In this vital role you will serve as a senior individual-contributor authority on semantic modeling, context engineering, and AI-first data science
—enabling high-performing classical ML, reinforcement learning–informed approaches, and generative AI systems through well-architected context
.

This role functions as an “AI Context Architect” (titled as a Data Scientist): a semantic architect who can define domain entities (e.g., payer, provider, patient, product, site, indication) and the relationships between them, so that data + context reliably drive model reasoning, retrieval, and downstream decisions. You will design the semantic foundations that make AI systems accurate, explainable, governable, and performant—partnering with engineering, product, security/compliance, and domain teams across R&D, Manufacturing, and Commercial

Roles & Responsibilities Semantic architecture & AI-first context modeling

• Define enterprise-grade semantic representations for healthcare/life-sciences concepts and specify how relationships and interactions are represented for AI consumption.
• Create and maintain semantic schemas / ontologies / knowledge-graph models that describe entities, attributes, constraints, and linkages—optimized for both analytics and AI reasoning.
• Establish context engineering standards
  : how data is shaped into prompts, tools, memory, retrieval indices, and structured outputs so models behave consistently across use cases.

Feature engineering & model performance (core emphasis)

• Lead feature engineering strategy tied directly to model performance
  , including feature definition, transformations, leakage prevention, stability monitoring, and explainability.
• Perform exploratory data analysis on complex, high-dimensional datasets to identify predictive signals and context variables that improve model robustness and generalization.

Context-aware ML, GenAI, and reinforcement learning–informed approaches

• Build and evaluate context-aware ML/GenAI solutions
  , integrating semantic layers with retrieval, tools, and structured outputs.
• Apply reinforcement learning concepts (reward modeling, policy optimization intuition, offline evaluation, exploration/exploitation framing) to improve decisioning, ranking, orchestration, and system behavior—without overfitting to short-term metrics.
• Prototype and benchmark algorithms and approaches (classical ML, deep learning, LLM-based reasoning) and advise on scalability and production readiness
  .

Retrieval, knowledge, and governance foundations

• Architect and implement retrieval and memory patterns (RAG, vector stores, knowledge graphs, session memory).
• Define data quality and semantic quality gates (entity completeness, relationship validity, taxonomy drift, grounding coverage) that impact downstream model reliability.

Cross-functional leadership

• Translate domain needs into semantic + AI roadmaps
  , aligning stakeholders on definitions, metrics, and tradeoffs.
• Act as a principal-level mentor and technical leader: establish standards, review semantic designs, and guide teams on best practices for context engineering and feature excellence.

What…