Associate Scientist, Data II

AbbVie•North Chicago, IL

3d•$65,500 - $125,500•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.