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Education: Bachelor's degree in Library Science, Information Systems, Linguistics, Computer Science or related.
Experience: Five years of experience in ontology, taxonomy, data science, data engineering, or machine learning. May substitute required education degree with additional years of equivalent experience on a one to one basis.
Preferred Experience:
• Five or more years of relevant work experience working in ontology and/or taxonomy roles, data science or data engineering.
• Proven skills in data retrieval and data research techniques
• Ability to quickly understand complex processes and communicate them in simple language
• Experience creating and communicating technical requirements to engineering teams
• Ability to communicate across a wide range of stakeholders including basic scientists, clinicians, and senior leadership (Director and VP levels)
• Knowledge of Semantic Web technologies (RDF/s, OWL), query languages (SPARQL) and validation/reasoning standards (SHACL, SPIN)
• Knowledge of open-source and commercial ontology engineering editors (e.g. Protege, Informatica)
• Detail-oriented problem solver who is able to work in fast-changing environment and manage ambiguity
• Proven track record of strong communication and interpersonal skills
• Proficient English language skills
• Proven track record of leading ontology/taxonomy implementation for the complete life cycle of a web product or technology
• Experience building ontologies in healthcare and semantic search spaces
• Experience working with schema-level constructs (e.g. higher-level classes, punning, property inheritance)
• Proficiency in SQL, SPARQL
• Familiarity with ontology manipulation programming libraries
• Exposure to data science and/or machine learning, including graph embeddings
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html
