Would you like to be a part of a Data Science & Artificial Intelligence (DS&AI) group who has direct strategic impact on drug development, playing a key role in getting medicines to patients?
At AstraZeneca, we are constantly pushing the boundaries of science to deliver life-changing medicines to patients, with a real passion for discovery and a pipeline to show for it. Here, you’ll have the opportunity to make a difference in people’s lives every single day.
AstraZeneca is investing in data management and analysis capabilities, through its long-term Growth Through Innovation Strategy.
The DS&AI team collaborates across R&D to drive innovation through data science and AI. Together we seek to:
- Improve our understanding of disease and uncovering new targets
- Transform R&D processes
- Speed the design and delivery of new medicines for patients
The R&D Data Office, within DS&AI, is a key organisation to deliver upon AstraZeneca’s strategy. Data Office operates a central capability, with R&D wide accountability, to ensure that we harness the power of data to drive innovative science. Data Office will govern data, drive data quality and ensure our data is readied for analytics, creating paved-paths for scientists to perform data-driven research, without compromising our legal restrictions or ethical principles.
Sustainable, FAIR (Findable, Accessible, Interoperable & Reusable) data is the lifeblood of AstraZeneca as a science-led, pure-play pharmaceutical company and critical for the delivery of the AstraZeneca’s corporate strategy, in which Data, AI and Digital is a foundational value driver for Innovative Science and a potential corporate differentiator.
Per the Data Discovery Directors guidance & oversight, the Data Discovery Manager will be accountable for the discovery of data assets, systems, and processes for a set of projects, that drive the journey towards making data FAIR.
- Scan external & internal data landscape to identify emerging data sources
- Assess and map the as-is state of Data Assets including information about:
- Repository location, type, size etc.
- Meta-data, reference/master data
- Data Lineage
- ETL processes, associated workflows
- Partners and Stakeholders
- Data QA / QC processes and artifacts
- Data standards, ontologies, vocabularies, formats etc.
- Data access and security
- Refresh schedules
- Data use specifications, licenses, & restrictions
- Generate standardized artifacts that capture the current state of data assets that can be used to make data assets FAIR
- Catalogue data assets not ingested into Data Office central repository
- Provide Subject-Matter-Expertise related to the current state of data assets
- Collaborate extensively with the Data Assets Management / Data Curation teams to inform the data / meta-data on-boarding & cataloguing processes, master data management, data curation, and access management.
- Assess impact and provide input for alternative-analysis for data vs. meta-data ingestion to a centralized repository
- Provide input to continuously improve processes to discover data assets and make scientific data FAIR and AI/Analytics-ready, so that data value grows over time. Secure value measurability, and scalability for changes in scope and demand.
- Collaborate with the Data Provisioning / Science Data Foundation / Data Strategy & Value / Data Governance teams to understand demand for data discovery and provide input for the planning and implementation of data discovery projects in consultation with the Data Discovery Director.
- Respond to internal and external drivers to accelerate R&D efficiency by proactively identifying emerging data sources of interest to AZ.
- Collaborate with other members of the Data Asset team, Data Office functional units, & the DS & AI community to aid the Data FAIRification process.
Education, skills and experience required
- Relevant degree in Information/Data Science, Computer Science, Informatics, IT, or other related discipline.
- Experience in the pharma and the life sciences sector
- Solid experience in business analysis and working with data repositories, catalogues, common data models, data standards, data landscape scanning, and analytic tools
- Strong system and business analytical skills with the ability to ask probing questions to elicit requirements and translate into documentable information
- Experience programming, testing, or supporting software development and integrations.
- Extensive experience in software requirements analysis and documentation
- Ability to learn new tools and technologies
- Experience in creating system and user documentation.
- Travel – willingness and ability to travel domestically & internationally.
- Resilience – ability to overcome and motivate others in the face of a changing environment