As a Knowledge Graph Engineer you will work as part of AbbVie’s Information Research RAIDERS team at the interface of bioinformatics, data engineering and natural language processing. You will work independently as part of a highly capable team helping to extract insights from data. You will apply your technical skills and experience with ontologies, graph databases and other forms of structured knowledge representation to exciting projects aimed at making a remarkable impact on patients’ lives.
Implement, transform and customize appropriate taxonomies and ontologies
Design and deploy ontologies, models and other structures in the representation of entities, relations and processes
Streamline and align the semantic relationships between different knowledge repositories.
Develop and reinforce business rules to classify content with ontology concepts
Create and maintain complex queries to classify data attributes, as well as design entity relationship diagrams (ERD) that meet business requirements
Assist, with the definition of, integration, translation (mapping), migration, and conversion strategies for new and existing knowledge content and data sources, including robust change control and versioning procedures
Research, evaluate, and recommend process improvements, including automated systems for knowledge capture, transformation, and presentation of information
Masters or PhD in Informatics/Information Management/Information Science/Computer Science or related education and 3-5 years’ experience or a Bachelor's degree and 7 years of experience.
Experience in developing, implementing and maintaining large, complex taxonomies and ontologies
Experience with terminology management and classification tools, such as Smartlogic Semaphore
Experience in modeling techniques for conceptual, logical, and physical data models
Experience in building models, ontologies, taxonomies, and other structures
Experience with AWS platform and cloud computing
Industry experience with programming and collaborating on software projects
Preferred: Practical experience in a scientific discipline such as biology, pharmacology or related, ideally a BS or related degree
Preferred: Familiarity with biomedical vocabularies and taxonomies, such as MeSH, SNOMED, ICD9/10, MedDRA etc.
Excellent understanding of semantic web standards such as RDF, SKOS, OWL, OBO and experience in validating and querying semantic models using SHACL/SPARQL
Advanced knowledge of modeling normalized data structures and manipulating data sets, and working with knowledge graphs
Python knowledge is essential
Some Java knowledge is preferre
Good understanding of search and indexing processes
Familiarity troubleshooting and mitigating metadata issues across enterprises
Knowledge of data format standards, including industry standards (e.g. XMI for interchange of XML), as well as database management systems (e.g. SQL Server, Oracle, NoSQL, MarkLogic)
Preferred: Background in data mining, natural language processing or machine learning
Conceptual and analytical thinker, ability to extract, analyze, and document complex data concepts and relationships
Excellent decision making
Strong organization, communication and interpersonal skills
Ability to work in an agile, fast-paced environment
Passion for exploratory projects, including proof-of-concept and proof-of-value engagements, and an ability to guide those concepts into production solutions
Aggressive drive to self-educate and learn new technologies and approaches