Senior Ontologist - Knowledge Graph & Identity

What You'll Do:

Ontology Design & Governance

• Own the end-to-end design, development, and versioning of Samba TV's core ontologies in RDF/RDFS/OWL - defining entity classes, properties, hierarchies, and constraints that accurately model Samba's data domain at scale

• Author and maintain SHACL shapes for post-load graph validation, consistency checking, and data quality enforcement

• Define and document derived-attribute schemas - genre affinity, brand affinity, topic affinity, lifecycle signals, and viewing summaries - and own the logical definitions that govern how raw events become durable graph attributes

• Establish ontology design standards, change management processes, and versioning practices; evaluate alignment with W3C standards and relevant industry schemas (Schema.org, EIDR, DDEX, W3C PROV)

• Lead ontology design reviews with product, data engineering, and data science stakeholders - articulating trade-offs between expressivity, scalability, and query performance clearly

Event-to-Ontology Derivation

• Define the aggregation and scoring logic that transforms raw TV viewership and web activity events into the durable affinities, summaries, and inferred signals that live in the graph

• Co-own derivation pipeline design with data engineering - specifying transformation logic, intermediate schemas, and validation checkpoints for Databricks/Spark pipelines that feed the materialized graph substrate

• Reason carefully about what belongs in the graph vs. what should remain virtualized in the data lake - balancing query performance against storage and refresh cost

Knowledge Graph Development & AI Integration

• Build and maintain production-quality knowledge graph pipelines in Python and SPARQL - well-tested, documented, and scalable to Samba's data volumes

• Design and implement entity resolution and record linkage pipelines that map real-world entities (content titles, devices, audiences, advertisers) to canonical knowledge graph nodes

• Develop enrichment workflows that integrate third-party data sources (metadata providers, identity vendors, web sources) into Samba's knowledge graph in a consistent, governed way

• Apply embedding-based and LLM-augmented approaches to ontology mapping, entity disambiguation, and semantic similarity problems

• Support content and semantic embedding pipelines that feed into the vector store and underpin GraphRAG-based AI solutions

Cross-functional Collaboration & Mentorship

• Partner with data engineering and platform teams to ensure the knowledge graph is integrated, queryable, and production-ready at scale

• Collaborate with product to translate business requirements into ontological and graph data model decisions

• Formally mentor Ontology Engineers and junior data scientists on semantic modeling, SHACL design patterns, and graph best practices

• Lead internal technical talks and workshops on ontology, knowledge graph, and semantic web topics

Who You Are:

Must-Haves

• 5–8 years of hands-on experience in ontology engineering, semantic data modeling, or knowledge graph development - with a demonstrable track record of production ontologies at scale

• Deep expertise in W3C semantic web standards: RDF, RDFS, OWL, SPARQL 1.1, and SHACL - with hands-on experience building and validating graph schemas in a production triplestore (Amazon Neptune, Stardog, GraphDB, Jena, or equivalent)

• Strong Python - production-quality, well-tested code; comfortable building data pipelines and graph processing workflows

• First-principles understanding of description logics, ontology design patterns, and the practical trade-offs between OWL expressivity and triplestore scalability

• Hands-on experience with entity resolution, record linkage, or deduplication at scale - mapping messy, multi-source real-world data to clean ontological representations

• Bachelor's degree required in Computer Science, Information Science, Computational Linguistics, Mathematics, or a related field; Master's or PhD strongly preferred

• Strong communicator - able to defend ontological modeling decisions in design reviews and explain trade-offs to non-specialist stakeholders

Strongly Preferred

• Hands-on experience with Amazon Neptune or Stardog - including data virtualization (Neptune Orion or Stardog Virtual Graphs) over data lake sources

• Experience designing aggregation and derivation logic that converts raw behavioral event data into durable, graph-resident derived attributes

• Domain knowledge in media, entertainment, or ad tech - TV viewership (ACR/STB), digital audience modeling (device graphs, identity resolution), or ad exposure data

• Familiarity with industry content and identity schemas: EIDR, Schema.org VideoObject, DDEX, or equivalent

• Experience with embedding models, vector databases (Milvus, Pinecone, Weaviate), and GraphRAG architectures (LangChain/LlamaIndex)

• Familiarity with GNN-based approaches to knowledge graph reasoning or entity resolution a plus

• Working knowledge of PySpark and Databricks for large-scale transformation pipelines