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Semantic Jobs (NOW HIRING)

Nestle

Expert Data Product (Semantic Layer)

Arlington, VA · On-site

$140K - $200K/yr

Enable semantic structures that support explainability, reuse, and high-performance access for analytics, ML pipelines, and GenAI applications * Drive training, documentation, and adoption of ...

Cotality

Senior Product Manager (Semantic Platform)

Irvine, CA · On-site

$135K - $179K/yr

The Senior Product Manager (Semantic Platform) is responsible for defining and evolving an internal platform that enables Cotality to capture, catalog, and evaluate enterprise semantic context. This ...

Cotality

Senior Product Manager (Semantic Platform)

Irvine, CA · On-site

$135K - $179K/yr

The Senior Product Manager (Semantic Platform) is responsible for defining and evolving an internal platform that enables Cotality to capture, catalog, and evaluate enterprise semantic context. This ...

AEG

Principal AI/ML Engineer, Semantic Data

Manhattan, NY · On-site

$235K - $260K/yr

The Principal AI/ML Engineer, Semantic Data will design and build the semantic intelligence layer that enables consistent understanding of fan data, business concepts, and operational workflows ...

eNett

Senior Director, Semantic Data Platform

$259K/yr

This leader will define and drive how WEX creates trusted semantic objects for our major business domains-such as customers, accounts, merchants, transactions, vehicles, payments, claims, and risk ...

Talanto

$231 - $284/hr

Build semantic business objects that bring together enterprise data, business context, relationships, metrics, derived attributes, rules, lineage, quality, and governance. * Establish a scalable ...

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All JobsSemantic Jobs

Semantic information

See salary details

$55.5K

$118.7K

$173.5K

How much do semantic jobs pay per year?

As of Sep 4, 2026, the average yearly pay for semantic in the United States is $118,674.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,500.00 and $133,500.00 per year, depending on experience, location, and employer.

What is a semantic job?

A Semantic job typically involves working with meaning and context in language, data, or technology. It may include roles in natural language processing (NLP), knowledge representation, search engine optimization (SEO), or semantic web technologies. Professionals in this field develop algorithms, ontologies, and models to improve understanding and classification of information. These jobs are common in AI, data science, and digital marketing industries.

What are the key challenges faced by semantic engineers when implementing knowledge graphs in large organizations?

Semantic Engineers often encounter challenges related to integrating disparate data sources, ensuring data quality, and aligning ontologies across departments. In large organizations, there can be legacy systems and inconsistent data formats, making it difficult to create a unified semantic model. Additionally, Semantic Engineers must collaborate closely with data architects, subject matter experts, and software developers to ensure the knowledge graph accurately reflects the organization's information needs and remains scalable as requirements evolve.

What are the key skills and qualifications needed to thrive as a semantic analyst, and why are they important?

To thrive as a Semantic Analyst, you need expertise in linguistics, natural language processing (NLP), data analysis, and a relevant degree such as linguistics, computer science, or information science. Familiarity with tools like Python, NLP libraries (e.g., NLTK, spaCy), and semantic annotation systems is typically required. Strong analytical thinking, attention to detail, and effective communication skills help you interpret complex language data and collaborate with technical teams. These competencies are vital to accurately extract, structure, and apply meaning from language data, driving insights and solutions in various industries.

What is the difference between Semantic vs Data Analyst?

AspectSemanticData Analyst
Required CredentialsBackground in linguistics, computer science, or related fields; knowledge of semantic web technologiesDegree in statistics, mathematics, or related fields; proficiency in data analysis tools
Work EnvironmentResearch-focused, often in tech or AI companies, working on language understandingBusiness or research settings, analyzing data to inform decisions
Industry UsageUsed in AI, NLP, and semantic web projectsUsed across finance, marketing, healthcare, and other sectors
Common Search/ComparisonSemantic vs Data Analyst

Semantic professionals focus on understanding and structuring meaning in language and data, often working with AI and NLP technologies. Data Analysts interpret data sets to generate insights for business decisions. While both roles involve data, Semantic roles emphasize language and knowledge representation, whereas Data Analysts focus on statistical analysis and reporting.

More about Semantic jobs

What cities are hiring for Semantic jobs?

Cities with the most Semantic job openings:

What are the most commonly searched types of Semantic jobs?

The most popular types of Semantic jobs are:

What states have the most Semantic jobs?

States with the most job openings for Semantic jobs include:

What job categories do people searching Semantic jobs look for?

The top searched job categories for Semantic jobs are:

Semantic jobs near you
Infographic showing various Semantic job openings in the United States as of August 2026, with employment types broken down into 90% Full Time, 5% Part Time, and 5% Contract. Highlights an 73% Physical, 6% Hybrid, and 21% Remote job distribution, with an average salary of $118,674 per year, or $57.1 per hour.

Associate Director, Semantic & Knowledge Engineering (2 Openings)

Novartis Pharmaceuticals Corporation

East Hanover, NJ • Hybrid

$268K/yr

Full-time

Medical, Retirement, PTO

Posted 12 days ago

Job description

Band

Level 5


Job Description Summary

#LI-Hybrid
Reporting to the Executive Director, Semantic and Knowledge Engineering, the Associate Director, Semantic and Knowledge Engineering designs, builds, and governs enterprise semantic models, ontologies, taxonomies, business vocabularies, knowledge graphs, metadata services, and semantic APIs. The role enables AI, analytics, enterprise search, interoperability, retrieval-augmented generation (RAG), agentic AI, contextual search, and reasoning engines by embedding reusable semantic capabilities into products and workflows across Strategy, Platforms & Transformation.
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.
There are 2 positions available.


Job Description

Key Responsibilities:

Enterprise semantic modeling and knowledge engineering

Design, develop, andmaintainenterprise ontologies, taxonomies, businessvocabularies, and semantic models thatestablishconsistent meaning across products, data, and AI capabilities.

Build and enhance knowledge graphs, metadata services, and semantic APIs that support AI, analytics, enterprise applications, and commercial decision enablement.

Semantic capabilities for AI and product workflows

Collaborate with Product, Applied AI, Analytics Engineering, and Platform Engineering teams to embed semantic capabilities into products, workflows, platforms, and reusable solution patterns.

Support semantic foundations for RAG, agentic AI, contextual search, reasoning engines, enterprise search, analytics, and interoperability initiatives.

Metadata governance andknowledgelifecycle management

Implement metadata governance, semantic quality controls, stewardship practices, and lifecycle management for enterprise knowledge assets.

Promote quality, consistency, reuse, transparency, and governed evolution of semantic assets across the SPT ecosystem.

Technology evaluation and architecture evolution

Evaluate emerging semantic technologies, standards, graph capabilities, and knowledge engineering approaches to inform the evolution ofthe enterpriseknowledge architecture.

Contribute reusable engineering patterns and technical guidance that reduce duplication and improve scalability across semantic and knowledge engineering work.

Technical mentorship and cross-functional collaboration

Mentor semantic and knowledge engineers while promoting reusable engineering patterns, technical excellence, disciplined documentation, and pragmatic implementation.

Communicate technical trade-offs, risks, dependencies, andrecommendations clearlyto product, engineering, analytics, AI, and business stakeholders.

Essential Requirements:

  • Education: Bachelor's degree in Computer Science, Information Science, Artificial Intelligence, Data Science, Bioinformatics, or a related discipline; advanced degree preferred.
  • 6+ years of progressive experience in semantic technologies, knowledge engineering, metadata management, data/information architecture, data product engineering, or AI-enabling data platforms.
  • Hands-on experience designing andmaintainingontologies, taxonomies, controlled vocabularies, business glossaries, semantic models, RDF/OWL, SKOS, SPARQL, graph databases, knowledge graphs, metadata catalogs, data lineage, and semantic APIs/services.
  • Experience building semantic assets that enable AI grounding, RAG/GraphRAG, enterprise search, contextual search, reasoning engines, analytics consistency, interoperability, and reusable product capabilities.
  • Working knowledge of data governance, stewardship models, provenance, quality controls, access/security controls, lifecycle management, privacy, and compliance expectations for enterprise knowledge assets.
  • Ability to translate complex business/domain concepts into reusable semantic models and partner effectively with Product, Applied AI, Analytics Engineering, Platform Engineering, Architecture, and business domain experts.
  • Strong engineering delivery discipline, including documentation, versioning, validation/testing of semantic assets, standards adherence, backlog execution, reusable patterns, and pragmatic implementation in agile/product teams.
  • Strong analytical, communication, stakeholder management, and collaboration skills, with the ability to explain semantic design choices, technical trade-offs, risks, and dependencies to technical and non-technical stakeholders.

Desirable Requirements:

  • Experience applying semantic technologies in pharmaceutical, healthcare, life sciences, commercial, clinical, medical, real-world data, or another regulated data environment.
  • Experience supporting generative AI, agentic AI, LLM-powered products, RAG/GraphRAG, vector databases, graph-enhanced retrieval, knowledge graph embeddings, enterprise search, or AI-ready semantic layers.
  • Familiarity with FAIR data principles, master/reference data modernization, metadata platform roadmaps, ontology governance forums, stewardship operating models, and enterprise knowledge architecture practices.
  • Experience with cloud-based data and AI ecosystems, graph/vector tooling, semantic layer technologies, API-based semantic services, and integration with enterprise search or analytics platforms.
  • Experience mentoring junior semantic/knowledge engineers, shaping reusable engineering patterns, and contributing to technical standards or communities of practice.
  • Advanced degree or relevant certification in Computer Science, Information Science, Data Science, Artificial Intelligence, Bioinformatics, Knowledge Engineering, Ontology Engineering, ora relateddiscipline.

Novartis Compensation Summary:

The salary for this position is expected to range between $176,400 and $327,600 per year.

The final salary offered isdeterminedbased 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,lifeand 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,holidaysand other leaves.


EEO Statement:

The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.


Accessibility and reasonable accommodations

The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or in order to perform the essential functions of a position, please send an e-mail to tas.nacomms@novartis.com call +1 (877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.
https://www.novartis.com/careers/careers-research/notice-all-applicants-us-job-openings


Salary Range

$176,400.00 - $327,600.00


Skills Desired

Artificial Intelligence (AI), Biostatistics, Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis

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