> Markdown version of [/jobs/ext/3344010-associate-data-scientist](https://www.wearedevelopers.com/jobs/ext/3344010-associate-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Associate Data Scientist - **Company:** AbbVie Inc. - **Location:** United States - **Contract:** Permanent contract - **Skills:** Data Analysis, Automation of Tests, BASIC (Programming Language), Continuous Integration, Data Infrastructure, Extract Transform Load (ETL), Query Languages, Document-Oriented Databases, Graph Database, Python (Programming Language), Neo4j, SAS (Software), Software Engineering, SPARQL, SQL Databases, Unstructured Data, Data Ingestion, Apache Spark, Generative AI, Git, Triple Store, Data Pipelines, Programming Languages - **Published:** September 11, 2026 - **Apply:** https://www.thejobnetwork.com/job/eed17512-abc4-45f9-bc0f-2571ba03f240/associate-scientist-data-ii ## About the Role * Bachelor's Degree with 2 years' experience; or Master's Degree equivalent education with no additional experience. * Exposure with software development life cycle including Git, testing, and basic CI/CD workflows. * Exposure with at least one graph database (e.g., Neo4j, Amazon Neptune, Tiger Graph, or an RDF triple store) and its query language (Cypher or SPARQL). * Exposure with any data analysis programming languages (e.g., SQL, Python & Apache Spark, SAS & R) including structured and unstructured data. Preferred: * Exposure working with entity resolution, record linkage, knowledge graph construction, vector databases and embeddings, and retrieval-augmented generation (RAG) or graph-RAG patterns is preferred. ## Description AbbVie Information Research is seeking an Associate Data Scientist with focus on Knowledge Graph Engineering to design, build, and maintain the semantic data infrastructure that connects information across our domains. In this role you will translate complex source data into well-modeled, interlinked graph structures, write the queries and pipelines that populate and validate them, and collaborate with domain experts, and platform teams to make our knowledge graph a reliable, query able source of truth. This role collaborates with solution architects, product owners, program managers, business analysts, infrastructure teams, and service providers to deliver data and analytics solutions., * Graph data modeling. Contribute to Design and refine labeled-property and/or RDF graph models - nodes, relationships, properties, and constraints - that accurately represent entities and their connections across source systems. * Pipeline development. Build, test, and maintain ingestion pipelines that extract data from relational, document, and file-based sources, transform it, and load it into the graph using batch and incremental patterns. * Query engineering. Write, optimize, and document Cypher queries for data loading, validation, entity resolution, and downstream retrieval, including support for graph-backed and retrieval-augmented applications. * Data quality and validation. Implement constraints, validation rules, entity resolution (e.g., SHACL or property checks), and automated tests that keep the graph consistent, traceable, and trustworthy. * Performance and operations. Monitor graph performance, tune indexes and queries, and assist with environment management across development, validation, and production tiers. * Collaboration and documentation. Partner with cross functional teams, analysts, and domain subject-matter experts to gather requirements; document data models, lineage, and design decisions clearly for technical and non-technical audiences. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Cyber Sleuth: Finding Hidden Connections in Cyber Data](https://www.wearedevelopers.com/videos/893-cyber-sleuth-finding-hidden-connections-in-cyber-data) - [Graphs and RAGs Everywhere... 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