Job Description Summary
#LI-Hybrid

Reporting to the VP, Applied AI, Data Science, and Commercial Intelligence, the Executive Director, Semantic and Knowledge Engineering leads the strategy, architecture, and implementation of the enterprise semantic layer and knowledge engineering capabilities that power NovaOS. This role is responsible for creating a trusted, reusable knowledge foundation that connects data, business concepts, ontologies, taxonomies, and AI agents to enable consistent reasoning, interoperability, and scalable enterprise intelligence.

The ideal location for this role is East Hanover but remote work may be possible (there may be some restrictions based on legal entity). Please note that this role would not provide relocation as a result. If associate is remote, all home office expenses and any travel/lodging to specific East Hanover for periodic live meetings will be at the employee's expense. The expectation of working hours and travel (domestic and/or international) will be defined by the hiring manager. This position will require 10% travel.

Job Description

Key Responsibilities:

• Define and execute the enterprise strategy for semantic technologies, knowledge engineering, and knowledge graph capabilities.

• Lead the development and governance of enterprise ontologies, taxonomies, business vocabularies, and semantic models that establish a shared understanding across the organization.

• Design and implement the enterprise semantic layer that enables trusted AI, consistent analytics, interoperability, and reusable business knowledge.

• Partner with Applied AI, Data Science, Product Management, Engineering, and Business teams to embed semantic capabilities into NovaOS products and AI solutions.

• Establish governance, metadata management, knowledge lifecycle processes, and quality standards for enterprise knowledge assets.

• Drive innovation in knowledge graphs, retrieval-augmented generation (RAG), semantic search, reasoning engines, and AI-enabled knowledge management.

• Build and lead a high-performing team of semantic engineers, ontology specialists, and knowledge engineers while fostering technical excellence and innovation.

Essential Requirements:

• Education: • Bachelor's or advanced degree in Computer Science, Information Science, Artificial Intelligence, Data Science, Bioinformatics, or a related discipline.

• 10+ years of experience in semantic technologies, knowledge engineering, enterprise architecture, data management, or AI platforms, including leadership experience.

• Deep expertise with ontologies, taxonomies, metadata, RDF/OWL, knowledge graphs, graph databases, semantic web technologies, and enterprise information architecture.

• Experience building enterprise semantic platforms that enable AI, analytics, search, and data interoperability at scale.

• Exceptional leadership, executive communication, and cross-functional stakeholder management skills.

Desirable Requirements:

• Experience applying semantic technologies to generative AI, agentic AI, RAG, and enterprise AI platforms.

• Experience in pharmaceutical, healthcare, or other highly regulated industries.

Novartis Compensation Summary:

The salary for this position is expected to range between $225,400 and $418,600 per year.

The final salary offered is determined based on factors like, but not limited to, relevant skills and experience, and upon joining Novartis will be reviewed periodically. Novartis may change the published salary range based on company and market factors.

Your compensation will include a performance-based cash incentive and, depending on the level of the role, eligibility to be considered for annual equity awards.

US-based eligible employees will receive a comprehensive benefits package that includes health, life and disability benefits, a 401(k) with company contribution and match, and a variety of other benefits. In addition, employees are eligible for a generous time off package including vacation, personal days, holidays and other leaves.

Salary Range
$225,400.00 - $418,600.00

Skills Desired
Artificial Intelligence (AI), Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Visualization, Deep Learning, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Mentorship, Stakeholder Engagement, Statistical Analysis, Time Series Analysis