Amgen · 1 day ago
Principal Data Scientist - AI Context Architect (Semantic & Context Engineering)
United States
Full-time
Remote
Lead/Staff
$163K/yr - $214K/yr
10+ years expAmgen is a leading biotechnology company dedicated to serving patients with serious illnesses. They are seeking a Principal Data Scientist to serve as a senior individual-contributor authority on semantic modeling and AI-first data science, focusing on enabling high-performing machine learning and generative AI systems through well-architected context.
BiotechnologyHealth CareManufacturingPharmaceutical
Responsibilities
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
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
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
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
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
Qualification
Required
Doctorate degree and 2 years of Data Science, Computer Science, Statistics, Applied Math, or related experience
Master's degree and 4 years of Data Science, Computer Science, Statistics, Applied Math, or related experience
Bachelor's degree and 6 years of Data Science, Computer Science, Statistics, Applied Math, or related experience
Associate's degree and 10 years of Data Science, Computer Science, Statistics, Applied Math, or related experience
High school diploma / GED and 12 years of Data Science, Computer Science, Statistics, Applied Math, or related experience
Preferred
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
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
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.
Company
Amgen
Amgen is a biotechnology company that develops and manufactures human therapeutics for various illnesses and diseases.
10001+ employees
H1B Sponsorship
Amgen has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (429)
2024 (485)
2023 (485)
2022 (540)
2021 (460)
2020 (444)
Funding
Current Stage
Public CompanyTotal Funding
$28.5B2022-12-12Post Ipo Debt· $28.5B
1983-06-17IPO
Leadership Team
Robert Bradway
Chairman and Chief Executive Officer
Jay Bradner
Executive Vice President of R&D, and Chief Scientific Officer
Recent News
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2026-01-09
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2026-01-09
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