Principal Data Scientist - AI Context Architect (Semantic & Context Engineering)

Amgen • Thousand Oaks, CA

1d

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. 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

Requirements

• Doctorate degree and 2 years of Data Science, Computer Science, Statistics, Applied Math, or related experience Or Master’s degree and 4 years of Data Science, Computer Science, Statistics, Applied Math, or related experience Or Bachelor’s degree and 6 years of Data Science, Computer Science, Statistics, Applied Math, or related experience Or Associate’s degree and 10 years of Data Science, Computer Science, Statistics, Applied Math, or related experience Or High school diploma / GED and 12 years of Data Science, Computer Science, Statistics, Applied Math, or related experience

Nice To Haves

• 10–12+ years applying data science in enterprise environments with demonstrated principal-level influence (or equivalent depth of expertise).
• Deep expertise in semantic modeling: ontologies, taxonomies, entity resolution, knowledge graphs, metadata and data contracts—built for operational use.
• Strong understanding of machine learning fundamentals and performance drivers, especially feature engineering and evaluation rigor.
• Practical experience implementing RAG / retrieval / vector search / knowledge graph solutions with clear governance patterns.
• Working knowledge of reinforcement learning concepts and how they apply to ranking, orchestration, personalization, or decision systems (even if not “pure RL” production).
• Proficiency in Python (and strong comfort with modern data/ML stacks); ability to collaborate effectively with engineering teams on production concerns.
• Exceptional stakeholder management: can drive alignment on, relationships, and metrics, and communicate tradeoffs clearly.
• Good-to-Have Skills Experience in biotech/pharma and healthcare commercial concepts (payer/provider dynamics, formulary/coverage).
• Familiarity with agentic/tool-using LLM patterns, prompt management, and structured outputs.
• Experience with feature stores, ML observability, and robust evaluation tooling.
• Publications, conference talks, or thought leadership in semantic AI / knowledge systems / enterprise GenAI.
• Soft Skills: Excellent analytical and troubleshooting skills.
• Strong verbal and written communication skills
• Ability to work effectively with global, virtual teams
• High degree of initiative and self-motivation.
• Ability to manage multiple priorities successfully.
• Team-oriented, with a focus on achieving team goals.
• Ability to learn quickly, be organized and detail oriented.
• Strong presentation and public speaking skills.
• Certifications Cloud/AI certifications (AWS/Azure/GCP) are a plus.

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.

Benefits

• A 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
• A 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Number of Employees: 5,001-10,000 employees