Join our Global Enterprise Architect (GEA) Team, where we support the global development of AstraZeneca AI products and work closely with teams across various business units. We design the fundamental elements of the AI world for AstraZeneca across our global customer businesses. We lead on enterprise-level, cross-organizational data architecture, for example around the definition and delivery of AI Products and alignment with AI platform and FAIR AI thinking across our businesses.
Accountabilities: In this role, you will collaborate with data scientists and other AI professionals to augment digital transformation efforts by identifying and piloting use cases. You will play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings. You will also audit AI tools and practices across data, models, and software engineering with a focus on continuous improvement.
Essential Skills/Experience:
Bachelor's Degree or equivalent number of years of experience in a Data Science or AI engineering or related field.
Experience in leading and delivering enterprise AI platform architectural thinking, and its practical application.
Experience in the use of conceptual and logical data modelling technologies.
Experience in defining and working with information and data regulatory governances.
The role holder will possess a blend of data/information architecture, analysis, and engineering skills.
Experience in known industry IT architectural patterns and IT architecture ways of working/methodologies (e.g. Amazon Bedrock, Amazon Q and Sagemaker).
Understands AI Platforms concepts and cloud-based containerization strategies for hybrid cloud environments.
Understanding the appropriate AI structure and technology based on business use case and completely familiar with AI lifecycles.
Manage small group of talent AI Architect and drive AI strategy and direction for Enterprise Landscape.
Desirable:
Post-graduate degree in MIS, AI.
Extensive experience in a senior AI/data Science and Data Engineering and AI architecture role with practical examples of designing and providing end-to-end and point data architecture designs or blueprints that have been delivered and implemented for substantial real-world use cases.
Ontological Design: Develop ontologies that define the relationships between different data entities.
Schema Creation: Design schemas that ensure the data is structured in a way that supports complex queries and analytics.
Standardization: Adhere to industry standards such as RDF (Resource Description Framework) and OWL (Web Ontology Language).
Source Identification: Identify and evaluate data sources relevant to the knowledge graph.
Data Mapping: Map data from various sources into the graph, ensuring consistency and accuracy.
SPARQL Queries: Develop complex SPARQL (SPARQL Protocol and RDF Query Language) queries to retrieve data.
Performance Tuning: Optimize the performance of queries to ensure fast and efficient data retrieval.
API Development: Create APIs that allow other systems and applications to interact with the knowledge graph.
Data Quality: Implement measures to ensure the accuracy, completeness, and consistency of the data.
Version Control: Manage different versions of the knowledge graph as it evolves.
Security: Ensure that access to the knowledge graph is secure and complies with data protection regulations.
Are you ready to make a meaningful impact on people's lives with life-changing medicines? Apply now and join us in our mission!
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