As an Ontology Engineer on Samba TV's Knowledge Graph & Identity team, you will build, maintain, and extend the knowledge graph schemas, derivation pipelines, and graph data models that underpin Samba's measurement and audience intelligence products. Working closely with the Senior Ontologist and peer data scientists, you will implement ontological frameworks in production, contribute to entity resolution and data enrichment pipelines, and help ensure the graph layer remains accurate, consistent, and production-ready.
This is a hands-on technical role. You are expected to write clean, production-quality Python and SPARQL, take ownership of well-scoped graph work streams, and grow your depth in semantic modeling under the guidance of senior team members.
This role reports to the Data Science Manager, Knowledge Graph & Identity.
What You'll Do:
Implement and extend Samba's RDF/RDFS/OWL ontology schemas in the graph database - adding entity classes, properties, and constraints in a consistent, governed way under the direction of the Senior Ontologist
Build and maintain SHACL validation shapes for post-load graph consistency checks; identify and triage data quality and schema violations
Support ontology versioning, change log documentation, and consistency checking across schema updates
Write efficient, well-structured SPARQL queries and graph traversals to support downstream data science and product use cases
Event-to-Ontology Derivation PipelinesContribute to the event-to-ontology transformation and derivation layer - building PySpark/Databricks pipelines that aggregate raw TV viewership and web activity events into durable graph attributes (genre affinity, brand affinity, topic affinity, viewing summaries, lifecycle signals)
Implement derivation logic specified by the Senior Ontologist and data science team; validate outputs against SHACL shapes before graph load
Support incremental refresh and update logic aligned with the graph's batch refresh cadence
Technical ContributionWrite production-quality Python - clean, well-tested, documented, and reusable by teammates
Work with PySpark and Databricks to process and transform high-volume data as part of graph pipeline development
Apply embedding-based approaches (semantic similarity, vector search) to entity matching and ontology alignment tasks
Contribute to team tooling, documentation, and reusable components that improve knowledge graph development efficiency
Collaboration & GrowthPartner closely with data engineering on pipeline design, data quality, and incremental ingestion patterns feeding the materialized graph substrate
Participate in ontology design reviews and cross-functional working groups
Work with product and operations teams to understand use case requirements and translate them into graph schema updates
Actively develop expertise in W3C semantic web standards, RDF-native graph databases, and entity resolution under the guidance of the Senior Ontologist
Who You Are:
2–4 years of hands-on experience in knowledge graph development, semantic data modeling, ontology engineering, or a closely related field
Working knowledge of W3C semantic web standards: RDF, RDFS, OWL, and SPARQL - with practical experience querying or building in at least one triplestore or graph database
Familiarity with SHACL or equivalent constraint and validation frameworks for graph data quality
Strong Python skills - clean, readable, production-quality code with testing and documentation
Solid understanding of data modeling fundamentals - entity-relationship design, taxonomies, hierarchies, and how to represent complex real-world relationships in structured form
Familiarity with entity resolution or data matching concepts - understanding of why the same real-world entity appears under different identifiers across data sources
Bachelor's degree required in Computer Science, Information Science, Mathematics, or a related field; Master's preferred
Detail-oriented and proactive about flagging data quality issues and schema inconsistencies
Strongly PreferredHands-on experience with Amazon Neptune or Stardog - or equivalent RDF-native triplestore; exposure to data virtualization (Neptune Orion or Stardog Virtual Graphs) a plus
Working knowledge of PySpark and Databricks - particularly for large-scale event aggregation and transformation pipelines
Familiarity with embedding models, vector search, or semantic similarity - applied to entity matching, ontology alignment, or knowledge graph enrichment
Experience with LLM APIs or RAG-based approaches applied to information extraction, entity disambiguation, or schema mapping
Domain knowledge in media, entertainment, or ad tech - content metadata, advertising entities, TV viewership data, or audience/identity data
Exposure to identity resolution, probabilistic record linkage, or device graph approaches
Skills Required
- Bachelor's degree in Statistics, Data Science, Computer Science, Mathematics or related field; Master's preferred
- 1 to 2 years hands-on data science experience
- Proficiency in Python with production-quality coding, testing, and documentation
- Strong SQL and PySpark skills for large datasets
- Experience with Databricks workflows and cloud platforms (AWS or GCP)
- Knowledge of core ML techniques and experimental design
- Familiarity with MLOps practices and reproducible model deployment
What We Do
Television remains a vibrant cultural influence and an essential source of entertainment and information worldwide. Tremendous growth in content choices, and viewing platforms that allow us to watch anything, anytime, on any screen, has actually made it harder for viewers to discover and keep up with all the great programming available. It’s also more competitive for content providers to keep your attention, and for marketers to make strong, measurable connections with their target consumers. Technology that improves the viewing experience, enables content discovery, and addresses audience fragmentation across screens will strengthen television’s business model and relevance to consumers. Data is at the center of any solution to make TV better. Samba TV's technology is built into Smart TVs and easily maps to smart phones and tablets. By recognizing what's on screen, Samba TV learns what viewers like and using machine learning algorithms, enables discovery of shows and actors in a whole new way. Likewise, our data and measurement products are transforming the way stakeholders across the media landscape are thinking about their business. Given the dramatic growth in streaming services, connected devices, time-shifting, and multi-screen viewership, our data products solve real problems and create a meaningful competitive advantage for our clients.
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