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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 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.
Job Responsibility:
Define enterprise-grade semantic representations for healthcare/life-sciences concepts
Create and maintain semantic schemas / ontologies / knowledge-graph models
Establish context engineering standards
Lead feature engineering strategy tied directly to model performance
Perform exploratory data analysis on complex, high-dimensional datasets
Build and evaluate context-aware ML/GenAI solutions
Apply reinforcement learning concepts
Prototype and benchmark algorithms and approaches
Architect and implement retrieval and memory patterns
Define data quality and semantic quality gates
Translate domain needs into semantic + AI roadmaps
Act as a principal-level mentor and technical leader
Requirements:
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
10–12+ years applying data science in enterprise environments with demonstrated principal-level influence
Deep expertise in semantic modeling: ontologies, taxonomies, entity resolution, knowledge graphs, metadata and data contracts
Strong understanding of machine learning fundamentals and performance drivers, especially feature engineering and evaluation rigor
Working knowledge of reinforcement learning concepts
Proficiency in Python
Exceptional stakeholder management
Nice to have:
Experience in biotech/pharma and healthcare commercial concepts
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
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
Cloud/AI certifications (AWS/Azure/GCP)
What we offer:
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