Ontologist

At AstraZeneca, we put patients first and strive to meet their unmet needs worldwide. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality. If you are swift to action, confident to lead, willing to collaborate, and curious about what science can do, then you’re our kind of person. As an  Ontologist  at AstraZeneca, you will play   a central role   in making enterprise-wide  Medical   data ready for advanced analytics and AI by designing, building, and evolving the semantic layer that connects and contextualises information across the organisation. Working closely with Medical, Data, Digital, and technical teams, you will develop ontologies, knowledge   graphs   ,   and   AZ   semantic standards that enable consistent interpretation, integration, and reuse of complex medical data at scale. This role will help transform fragmented data into a structured, interoperable foundation that supports insight generation, data-driven decision-making, and innovative AI applications, while promoting data quality, consistency, and governance across markets and therapeutic areas .      •

Design and develop ontologies spanning HCPs, KOLs, accounts, patient journeys, indications, brands, campaigns, channels, scientific platforms, congress activities, and evidence tiers — supporting consistent segmentation, targeting, scientific engagement tracking, and performance measurement across markets.   •

Create and maintain ontological patterns for MSL interactions, KOL engagement, medical content tagging, claim–reference linking, consent, preferences, touchpoints, and next-best-action — enabling personalised scientific and promotional engagement, attribution modelling, MLR/PRC workflow support, and integrated medical-commercial reporting.   •

Apply external vocabularies and identifiers (e.g., NPI,   OneKey , SNOMED CT, ATC,   RxNorm , MedDRA,   MeSH ) within ontology models, and incorporate privacy, consent, and regulatory semantics to support GDPR, CCPA, ABPI Code, EFPIA Code, and global policy requirements.   •

Work closely with Medical Affairs, MSL teams, brand teams, analytics, Martech, architecture, governance/assurance, and data engineering to gather requirements,   validate   ontology designs, curate terminology, and support adoption across business users and consumers.   •

Contribute to ontology versioning, change management, lineage documentation, and quality assurance processes — ensuring models   remain   accurate , consistent, and aligned to enterprise standards.

:    Bachelor's   or   Master's degree in Information Science , Computer Science, Bioinformatics, Data Science, Life Sciences, or a related field — or equivalent professional experience.   •

Experience developing ontologies, taxonomies, controlled vocabularies, or knowledge graphs within a structured delivery environment.   •

:    Working   knowledge of RDF, OWL, SKOS, SPARQL, and graph-based knowledge representation.   •

: Experience using semantic models to integrate and standardise data across multiple systems and sources.   •

: Ability to work across multiple stakeholder groups including architecture teams, business users/consumers, and governance/assurance functions.   •

Understanding of pharmaceutical, clinical, scientific, or healthcare data domains — particularly where medical and commercial data intersect.   •

Experience applying data standards, controlled terminology, and governance practices to improve consistency and reuse.   •

Ability to translate business needs into clear semantic solutions and explain ontology concepts to non-technical audiences.   •

Experience working within multidisciplinary teams in agile or iterative delivery environments.

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Familiarity with platforms such as Protégé,   TopBraid ,   Metaphactory ,   PoolParty ,   Stardog , Neo4j, Veeva Vault/CRM metadata, Informatica, or Collibra.   •

Exposure to terminologies such as SNOMED CT, MedDRA, LOINC, ICD,   MeSH , ATC, WHO-DD, or CDISC.   •

Understanding of how ontologies and semantic layers support analytics, search, and AI use cases.   •

Familiarity with enterprise data platforms, metadata management, and data architecture principles.

07-sie-2026

15-sie-2026 AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.