Astrazeneca
Pharmaceutical
Ontologist
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Ontologist at Astrazeneca. Skills: Ontology development, Knowledge graphs, Semantic layer, Medical data. Design and develop ontologies spanning HCPs, KOLs, accounts,. Create and maintain ontological patterns for MSL interactions,”
What You'll Achieve.
Enable consistent interpretation, integration, and reuse of complex medical data at scale; Transform fragmented data into a structured, interoperable foundation; Support insight generation; Support data-driven decision-making; Support innovative AI applications; Promote data quality, consistency, and governance
Industry & Context.
Insight generation; Data-driven decision-making
What They're Looking For.
Must Have
Bachelor's or Master's degree in Information Science, Computer Science, Bioinformatics, Data Science, Life Sciences, or related field, or equivalent professional experience, Demonstrable experience in ontology development, taxonomy design, or semantic data modelling, Experience developing ontologies, taxonomies, controlled vocabularies, or knowledge graphs, Working knowledge of RDF, OWL, SKOS, SPARQL, and graph-based knowledge representation, Experience using semantic models to integrate and standardise data, Ability to work across multiple stakeholder groups, Understanding of pharmaceutical, clinical, scientific, or healthcare data domains, Experience applying data standards, controlled terminology, and governance practices, 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
Nice to Have
Experience working in pharmaceutical, biotech, or life sciences environments, Familiarity with 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
What You'll Do.
Design and develop ontologies spanning HCPs
Create and maintain ontological patterns for MSL interactions
Apply external vocabularies and identifiers within ontology models
and regulatory semantics
Validate ontology designs
Support adoption across business users and consumers
Contribute to ontology versioning
Perform quality assurance processes
How You'll Work.
Team & Collaboration
Medical Affairs teams; MSL teams; Brand teams; Analytics teams; Martech teams; Architecture teams; Governance/assurance teams; Data engineering teams; Multidisciplinary teams
Communication Scope
Explain ontology concepts
Process & Methodology
Agile delivery, Iterative delivery
Full Job Description
**Ontologist** **Location: Warsaw** **Hybrid model:** **3 days per week from office** 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. * **Medical-commercial ontology design and development: **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. * **Medical-commercial engagement modelling: **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. * **Standards, interoperability, and comp
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