---
title: 'Senior Ontologist - Knowledge Graph & Identity at Samba'
canonical: 'https://feeny.ai/job/senior-ontologist-knowledge-graph-identity-samba-warsaw-maadbvvzpzgv'
type: 'job'
last_seen: '2026-09-14'
---

# Senior Ontologist - Knowledge Graph & Identity at Samba

- **Company:** Samba
- **Location:** Warsaw, Poland
- **Compensation:** PLN 250k–PLN 380k
- **Employment:** internship
- **Work type:** hybrid
- **Posted:** 2026-09-03
- **Last confirmed live:** 2026-09-14
- **Apply:** https://jobs.lever.co/sambatv/1a1f3a6b-cb8b-4d01-a865-b0fd61981525

## Job description

Samba is a media intelligence company. We know what the world is watching, reading, and thinking about — in real time, at scale, across every screen. Our data exists with the consent of over a billion people, organized into the most complete picture of consumer attention ever built. The biggest brands in the world use that picture to make smarter decisions. We think it’s the most interesting data asset on the planet, because it’s the most culturally relevant.

## 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

Samba is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.  We strive to empower connection with one another, reflect the communities we serve, and tackle meaningful projects that make a real impact.

Samba may collect personal information directly from you, as a job applicant, Samba may also receive personal information from third parties, for example, in connection with a background, employment or reference check, in accordance with the applicable law. For further details, please see Samba's Applicant Privacy Policy. For residents of the EU , Samba Inc. is the data controller.

## About Samba

## Company Overview
- **One-liner**: Samba (formerly Samba TV) is a media intelligence company that uses AI and first-party deterministic data from 1.5 billion people to power cross-screen TV and digital advertising measurement, targeting, and analytics for the world's largest brands, publishers, and platforms.
- **Entity Type**: Private (Series B)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2008
- **Founders**: Ashwin Navin (Co-founder & CEO), Alvir Navin (Co-founder & COO)

## Core Business
- **Primary Industry**: Media Intelligence / Advertising Technology / Data & Analytics
- **Target Customers**: B2B — primarily Enterprise (global brands, agencies, publishers, and platforms like Disney, Havas Media, and "the vast majority of top global brands")
- **Mission Statement**: Unite TV, digital, and behavioral data through AI to power faster, more accurate media decisions.

## Products & Services
- **Samba Analytics**: Independent cross-screen measurement using first-party data for deduplicated reach, frequency, and performance outcomes. Allows optimization of campaigns while they are still in-flight.
- **Samba Audiences**: Exclusive audience segments and Private Marketplaces (PMPs) built from Samba's deterministic cross-screen signals for effective targeting.
- **Samba Knowledge Graph**: AI-powered mapping of interests, behaviors, and purchase intent, linking content to 1.5 billion real people, devices, and households.
- **Samba ID**: A universal identity solution linking content to real people and devices across screens.
- **Samba AI**: Contextually analyzes and indexes all media content, including brand logo detection on TV.

## Market Standing
- **Valuation**: Not publicly disclosed
- **Total Funding**: $75.7M USD across 9 rounds
    - Last known round: Series B ($30M, June 2017, led by Union Grove Venture Partners and Disney Accelerator)
    - Other notable rounds: Series A ($7M, February 2012, led by August Capital)
- **Key Metric**: Owns first-party TV and web data for 1.5 billion people globally.
- **Notable Investors/Partners**: August Capital, Union Grove Venture Partners, Disney Accelerator. Strategic clients include Disney, Havas Media, and "every platform and publisher you need."
- **Growth Signals**: Acquired Semasio in 2024, becoming the only provider owning both TV and web first-party data. Rebranded from "Samba TV" to "Samba" in 2026. Global workforce of 251 employees (+20.1% YoY growth). Operations span 6 countries with 13 offices.

## Competitive Advantages
- **First-Party Data Moat**: Nearly two decades of proprietary, deterministic data from smart TVs (24 brands, 48M devices) and web (1.5B people), creating a unique and un-copyable dataset.
- **Full-Funnel Integration**: The only provider to combine TV and web first-party data (after acquiring Semasio), offering planning, measurement, and activation in one platform.
- **AI-Powered Knowledge Graph**: Goes beyond traditional media measurement by mapping interests, behaviors, and purchase intent, enabling contextual analysis without relying on third-party cookies.

## Strategic Focus
- **Platform Expansion**: Evolving from a TV data provider into a comprehensive "media intelligence" company (rebranded in 2026).
- **Cross-Screen Unification**: Connecting TV and digital data to provide a complete view of audience behavior across all screens.
- **AI & Contextual Intelligence**: Deepening investment in AI for content analysis, brand detection, and predictive audience targeting.
- **Global Scale**: Operating across six continents and expanding direct partnerships with major platforms and publishers.

## Why Work Here
- **Founder-Led Culture**: Described as having "startup intensity and enterprise impact," with a "culture that values ownership over hierarchy and outcomes over optics."
- **Global & Distributed**: Teams span San Francisco (HQ), New York, London, Hamburg, Sydney, Los Angeles, Chicago, Austin, Warsaw, Taipei, and Amsterdam. Officed with a global, collaborative ethos.
- **Engineering & Data-First**: Ships products that "touch billions of signals daily." A strong emphasis on data science, engineering, and product roles.
- **Compensation & Benefits**: Competitive base salary, equity participation, performance bonuses, generous parental leave, flexible PTO, annual learning budgets, home office stipend, and best-in-class hardware.
- **Culture Values** (from company career page): Determined & Resilient, Self-Aware, Naturally Curious, Low Ego, Results Oriented.
- **Employee Rating**: 3.9/5.0 on LinkedIn (172 reviews), with Work-Life Balance at 3.9, Compensation at 3.8, and Culture at 3.8.

## Sources
1. [samba.com](https://samba.com/about-samba) — About Samba / Company History
2. [samba.com](https://samba.com/careers) — Careers Page / Culture & Benefits
3. [samba.com](https://samba.com/) — Home Page / Product & Platform Overview
4. [linkedin.com/company/sambatv](https://www.linkedin.com/company/sambatv) — LinkedIn Profile (Company Details, Size, Financials)
5. [jobs.lever.co/sambatv](https://jobs.lever.co/sambatv) — Lever Careers Page (Open Roles)

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