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Team Lead, Analytics Data Preparation

AstraZeneca
Job LocationCambridge
EducationNot Mentioned
SalaryCompetitive salary
IndustryNot Mentioned
Functional AreaNot Mentioned
Job TypePermanent, full-time

Job Description

Team Lead, Analytics Data Preparation – R&D Data OfficeLocation(s) - Cambridge (UK); Gothenburg (Sweden); Gaithersburg (USA)Salary - Competitive salary and company benefitsWould 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, 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-driven research, without compromising our legal restrictions or ethical principles.Within Data Office, you will lead the Analytics Data Preparation team. This technical team is dedicated to preparing bespoke datasets and data assemblies to-order to accelerate data science activities across AstraZeneca’s R&D portfolio.This is a leadership and management position. You will work with the Head of Data Provisioning and will use their guidance and strategic leadership to run a global Analytics Data Preparation team, operating as manager, lead analyst/consultant and senior technical lead. The efficient operation and staffing of the Analytics Data Preparation team will be your responsibility and you will be accountable for efficient and valuable team delivery. In this role, you will be empowered to devise, own and run an agile delivery practice for creation high-quality, bespoke datasets and new data assemblies, where the team’s responsibilities span from marketing their team capabilities, project definition and planning, technical requirements analysis to design, development, UAT and handover of data products to support fast-paced, innovative and high-value data science projects.As no two projects will be the same, you will use creativity and problem-solving skills to support the Analytics & AI Data Preparation team as a senior data developer, providing technical and data-scientific guidance and oversight to ensure successful delivery. As a technical leader within AstraZeneca Data Science & AI you will be expected to contribute towards strategic activities, such as enhancement plans for new data infrastructure and data science tooling.Your leadership of the Analytics Data Preparation team, will enable data-driven and scientific innovation to proceed at pace and at scale within AstraZeneca, ultimately leading to the development of new, life-changing medicines for patients. Typical Accountabilities
  • Ultimately, we need you to be accountable for ensuring that the Analytics Data Preparation team are accelerating data science projects.
  • You will market your team’s services to relevant technical groups and consult with them on potential projects that can be supported.
  • You will be responsible for good technical analysis practices to ensure that requirements are captured.
  • As a technical leader in the Data Provisioning Team, we require you draw on passion for ground breaking solutions to drive better business results, enable entirely new data science paradigms and operate more efficiently.
  • Team line management, inspiring and driving team spirit and success.
  • Resource management and recruitment within your team to build a group with the right blend of personalities, scientific, analytical and technical skills.
  • Technical delivery model design and day-to-day delivery leadership e.g., acting as a team Scrum-master and running associated scrum ceremonies to create efficient ways of driving business value from the team’s effort.
  • Own delivery quality including a quality ‘manual’ and necessary operating processes that ensures your team meets AZs broader commitment to scientific and technical leadership.
  • Operating as a senior technical delivery team member, particularly for high-priority or scientifically and technically complex projects, where your knowledge and design skills will be crucial.
  • Supporting IT data engineering planning to consider how to productionise and automate high-value data pipelines.
  • Own communication strategy to ‘market’ and provide help and training (whether hands-on, documents or other media) for R&D users to make the most of the services your team offers, as both your team and the needs of R&D evolves.
    • Education, skills and experience required
  • Leadership, teamwork and great communication skills are essential.
  • Great working knowledge of applied mathematical, machine learning and AI techniques and workflows.
  • Proven experience in leading agile delivery to achieve valued business outcomes e.g., certified and experienced Scrum-master.
  • Team and resource management experience including planning, hiring and extending teams using contingent staff.
  • Excellent skills in one or multiple scripting and programming languages to (Python, R, SQL etc).
  • Experience of data analysis profiling, investigating, interpreting and documenting data requirements, e.g. data modelling techniques and hands on modelling experience.
  • Knowledge of data-centric FAIR-ification approaches, such as linked data, knowledge graphs, use of standards, vocabularies and ontologies.
  • Knowledge of a range of data & analytics patterns and workflows, where technical aspects may include including distributed computing (Hadoop/Spark), NoSQL, virtualization, data streaming, container technology (e.g., Docker), traditional warehousing etc.
  • In-depth knowledge and experience of the Big Data ecosystem. This might include: HDFS, Hadoop, Parquet, Spark, Spark streaming, HBase, HIVE, PIG, Presto, Sqoop, Mesos etc.
  • Experience in the biomedical and pharma sectors as a Data Scientist or Data Engineer a definite advantage.Knowledge of AWS Cloud services that our IT devops data engineering teams manage and our data scientists employ would be an advantage. We currently use EMR, SQS

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