Associate Director, Semantic & Knowledge Engineering

Novartis

Confirmed live today High trust

Hybrid

Quick summary

Work type: Hybrid
Location: East Hanover, NJ
Salary: $176,400–$327,600 / yr
Employment: Full-time
Posted: 65 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $237k

This role $252k

$158k $346k

below market most similar roles pay here above market

This role pays more than 59% of similar roles. Most pay $193,830–$279,762 — the blue band above. At the midpoint, this role pays about $252k versus about $237k for comparable roles.

Based on 240 similar postings.

Employer

About Novartis

Novartis is a global biopharmaceutical company that researches, develops, manufactures, and markets prescription drugs in areas including oncology, immunology, neuroscience, and cardiology. Industry: Biopharmaceuticals

Novartis currently has 17 open roles on FindRole.

Listed pay typically runs $176,400–$327,600 across 17 roles with salary data.

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At a glance

TL;DR · Associate Director, Semantic & Knowledge Engineering

Role Posting Log in to save

The Associate Director, Semantic & Knowledge Engineering joins the Strategy, Platforms & Transformation team to design, build, and govern enterprise semantic models. This role involves developing ontologies, taxonomies, business vocabularies, knowledge graphs, metadata services, and semantic APIs. The successful candidate will embed reusable semantic capabilities into products and workflows to support AI, analytics, enterprise search, retrieval-augmented generation, agentic AI, and reasoning engines. Key responsibilities include implementing metadata governance, quality controls, and lifecycle management for knowledge assets while mentoring engineers. Required technical skills include RDF/OWL, SKOS, SPARQL, graph databases, and Python. The role addresses the business problem of establishing consistent meaning across products and data, ensuring interoperability and scalability. Experience in pharmaceutical or regulated data environments, specifically with vector databases and FAIR data principles, is highly desirable.

Skills

Knowledge Engineering Ontology Engineering RDF OWL SKOS SPARQL Knowledge Graphs RAG GraphRAG Vector Databases Python Metadata Management Data Governance Data Lineage Machine Learning Deep Learning Graph Algorithms FAIR Data Principles Agile

What you'll do

Design, develop, and maintain enterprise ontologies, taxonomies, business vocabularies, and semantic models.
Build and enhance knowledge graphs, metadata services, and semantic APIs to support AI and analytics.
Embed semantic capabilities into products, workflows, and platforms to support RAG and agentic AI.
Implement metadata governance, semantic quality controls, and lifecycle management for enterprise knowledge assets.
Evaluate emerging semantic technologies and standards to inform the evolution of the enterprise knowledge architecture.
Create reusable engineering patterns and technical guidance to improve scalability and reduce duplication.
Mentor semantic and knowledge engineers on technical excellence, documentation, and pragmatic implementation.
Communicate technical trade-offs, risks, and dependencies to both technical and non-technical stakeholders.

What we're looking for

Bachelor's degree in Computer Science, Information Science, Artificial Intelligence, Data Science, Bioinformatics, or a related discipline.
Advanced degree in a related discipline (preferred).
6+ years of progressive experience in semantic technologies, knowledge engineering, metadata management, data architecture, or AI-enabling data platforms.
Hands-on experience designing and maintaining ontologies, taxonomies, RDF/OWL, SKOS, SPARQL, graph databases, and semantic APIs.
Experience building semantic assets for AI grounding, RAG/GraphRAG, enterprise search, reasoning engines, and analytics consistency.
Working knowledge of data governance, stewardship models, provenance, quality controls, privacy, and compliance for enterprise knowledge assets.
Experience applying semantic technologies in pharmaceutical, healthcare, life sciences, or other regulated data environments (preferred).
Experience supporting generative AI, LLM-powered products, vector databases, and knowledge graph embeddings (preferred).

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