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

## **Career Category**Information Systems## ## **Job Description**## Join Amgen’s Mission of Serving PatientsAt 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 we expect of you**We are all different, yet we all use our unique contributions to serve patients. The professional we seek will have these qualifications.**Basic Qualifications:**Doctorate degree and 2 years of Data Science, Computer Science, Statistics, Applied Math, or related experience## OrMaster’s degree and 4 years of Data Science, Computer Science, Statistics, Applied Math, or related experience## OrBachelor’s degree and 6 years of Data Science, Computer Science, Statistics, Applied Math, or related experience## OrAssociate’s degree and 10 years of Data Science, Computer Science, Statistics, Applied Math, or related experience## OrHigh school diploma / GED and 12 years of Data Science, Computer Science, Statistics, Applied Math, or related experience**Preferred Qualifications:*** **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.## ## **What you can expect of us**As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.The expected annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications.In addition to the base salary, Amgen offers a Total Rewards Plan, based on eligibility, comprising of health and welfare plans for staff and eligible dependents, financial plans with opportunities to save towards retirement or other goals, work/life balance, #J-18808-Ljbffr