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Senior Data Asset Management Lead

Местоположение Кембридж, Англия, Великобритания Идентификатор вакансии R-095191 Дата публикации 12/20/2020

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 chance to create 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, crafting paved-paths for scientists to perform data-led research, without compromising our legal restrictions or ethical principles.

Sustainable, FAIR (Findable, Accessible, Interoperable & Reusable) data is the lifeline 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.

Under the supervision of the Data Asset Management Director, you will be accountable for:

  • Designing/ optimizing processes and workflows for the onboarding, cataloguing, & management of data assets. Vision is to make our data assets FAIR while being agile to the needs of our business partners and processes should support/advance our overall vision.
  • Efficiently leading the life-cycle of existing/new data assets in our portfolio (ingestion, transformation, publishing, provisioning, performance measurement, archival).

Typical Accountabilities

  • Analyse current-state information related to existing processes & workflows for running the life-cycle of data assets. Drive improvements / enhancements to the processes for realising value in our day to day operations and business initiated strategic projects.
  • Create the Data Management Plan (DMP) for efficiently managing data assets throughout the entire life-cycle:
  • Ensure the metadata gathered in the DMP supports our FAIR vision, is impactful and socialised/understood by the operational teams to realize the benefits across the value chain.
  • Drive intelligent automation to ensure the metadata captured is synced to the data catalog and actioned for operational use-cases where possible.
  • Validate /Improve DMP over time with feedback learned from day to day business topics.
  • Measure improvements and value of our initiatives with the broader organisation.
  • Implement / Provide input for the design of efficient processes and specifications for:
  • on-boarding of data / meta-data into a centralised data repository
  • cataloguing of data assets
  • data asset management including optimal data standards, refresh schedules
  • workflow management to ensure access to the right data assets and versions
  • automation to enable operational scale-up
  • Collaborate with IT partners for the implementation of data / meta-data on-boarding, and cataloguing activities including the development of minimum meta-data models for each data asset type.
  • Data Provisioning / Science Data Foundation / Data Strategy & Value teams to understand demand for data / meta-data on-boarding and management and provide input for the planning and implementation of data asset management activities in consultation with the Data Asset Management Director
  • Inform the decision-making process for data vs. meta-data ingestion to a centralized repository.
  • Continuously improve processes to lead 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 other members of the Data Asset team, Data Office functional units, & the DS & AI community to aid the Data FAIRification process.


  • Relevant degree in Information/Data Science, Computer Science, Informatics, IT, or other related subject area.
  • Experience in the pharma and the life sciences sector
  • Demonstrable experience in data asset management and data ownership - Band E
  • Good operational understanding of data repositories, ETL processes, data management principles, common data models, data standards, and analytic tools.
  • Experience programming, testing, or supporting software development and integrations for the on-boarding, cataloguing and management of data assets.
  • Extensive experience working with IT partners in the implementation of data asset management projects
  • 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 empower others in the face of a changing environment

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 authorisation and employment eligibility verification requirements.

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