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Resourceful

Data Scientist

Resourceful, Seattle

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This range is provided by Resourceful. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$150,000.00/yr - $175,000.00/yr

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Job Title: Senior Data Scientist – GenAI & Knowledge Graphs

Location: Seattle, WA, US

Department: AI & Data Science

Employment Type: Full-time

Job Summary

We are looking for a Senior Data Scientist with hands-on expertise in building Generative AI applications using Knowledge Graphs, Graph Databases, and multi-agent systems. The ideal candidate will have strong experience in LLM-driven development, agentic AI workflows, and semantic data modeling using OWL and RDF.

Key Responsibilities

  • Build and maintain graph-based data pipelines using technologies like Neo4j, Amazon Neptune, or Stardog.
  • Design and implement knowledge graphs and ontologies using OWL, Protégé, TopBraid, or similar tools.
  • Integrate knowledge graphs with GenAI pipelines, improving context grounding and retrieval for LLM-based applications.
  • Develop and orchestrate multi-agent systems using LangGraph, CrewAI, or AutoGen, including agent-to-agent (A2A) communication, memory modules, and reasoning chains.
  • Leverage MCP servers and agent runtime engines to deploy agent-based GenAI applications for real-world scenarios such as customer support, content synthesis, and document analysis.
  • Work on RAG architectures involving embedding models, vector stores (e.g., FAISS, Pinecone), and structured semantic layers.
  • Collaborate with engineers and junior data scientists on project delivery and model deployment.
  • Write clean, reusable code in Python using ML/LLM frameworks such as LangChain, HuggingFace, and OpenAI SDKs.

Required Skills & Experience

  • 5–8 years of experience in data science, with 2+ years in LLM/GenAI development.
  • Hands-on experience with GraphDBs (Neo4j, Neptune, TigerGraph) and SPARQL or Cypher.
  • Proficiency in ontology modeling using OWL/RDF and familiarity with semantic reasoning tools.
  • Strong experience in building agentic AI applications, including use of LangGraph, AutoGen, or CrewAI.
  • Understanding of multi-agent communication protocols (A2A), agent memory, and orchestration layers.
  • Solid understanding of data wrangling, NLP, and embedding models (sentence-transformers, OpenAI embeddings, etc.).
  • Python proficiency and experience with REST APIs, data processing libraries (pandas, NumPy), and JSON-LD.

Preferred Qualifications

  • Master’s degree in Data Science, Computer Science, AI/ML, or a related field.
  • Knowledge of cloud-native deployment is a plus (though not mandatory).
  • Open-source contributions, blog posts, or internal project showcases in the GenAI/Knowledge Graph space.

Seniority level

  • Seniority level

    Mid-Senior level

Employment type

  • Employment type

    Contract

Job function

  • Job function

    Consulting

  • Industries

    IT Services and IT Consulting

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