Job Description Summary
#LI-Hybrid

Reporting to the Executive Director, Semantic and Knowledge Engineering, the Director, Semantic and Knowledge Engineering leads the design, implementation, and governance of enterprise semantic models, ontologies, knowledge graphs, and metadata frameworks that power NovaOS. This role partners across Applied AI, Data Science, Product, and Engineering to establish trusted semantic assets that enable interoperable data, AI reasoning, retrieval-augmented generation (RAG), 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:

• Lead the design and implementation of enterprise ontologies, taxonomies, business vocabularies, and semantic models.

• Develop and maintain knowledge graphs and semantic services that support AI, analytics, search, and business applications.

• Partner with AI, Data Science, Product, and Engineering teams to integrate semantic capabilities into NovaOS products and platforms.

• Establish metadata standards, semantic governance processes, and quality controls for enterprise knowledge assets.

• Support AI initiatives by developing semantic foundations for RAG, agentic AI, reasoning engines, and contextual search.

• Evaluate emerging semantic technologies and recommend improvements to the enterprise knowledge architecture.

• Lead and mentor semantic and knowledge engineers while promoting reusable engineering patterns and technical excellence.

Essential Requirements:

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

• 8+ years of experience in semantic technologies, knowledge engineering, metadata management, or enterprise information architecture.

• Hands-on expertise with ontologies, RDF/OWL, knowledge graphs, graph databases, metadata management, and semantic web technologies.

• Experience implementing semantic solutions that enable AI, analytics, interoperability, and enterprise search.

• Strong leadership, communication, and stakeholder management skills with experience leading cross-functional technical initiatives.

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 $194,600 and $361,400 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
$194,600.00 - $361,400.00

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