AstraZeneca Macclesfield, England, UK
As a Data Scientist within our Operations Quality function you will work with meaningful data to generate impactful evidence and insights on our Operations Quality System and the impact Quality System performance is having on our wider Operations and Enterprise goal. You will support advances in understanding of our Global Business Processes and enable Business Process Owners to drive targeted improvement activities to meet and exceed wider Operations business priorities. You will collaborate with peers within the Quality function and across the organization to develop data science generation strategies, identify gaps and data sources, design and execute studies, and implement analyses to address business and product related questions. The data will be varied in type, covering Quality System, Manufacturing, Forecasting, Financial, HR and Health & Safety Data across Late Stage Drug Development and into Commercial Manufacturing. The purpose of Operations Quality Business Intelligence Team is to partner with Operations Quality Business Process Owners, Operations Quality Site Contacts and Analytics Functions across the AstraZeneca Operations Organization to improve data capture, management, reporting and analytics. The ambition of the Operations Quality Business Intelligence Team is to provide world-class Business Intelligence and Advanced Analytics delivery that accelerates improvements across our Pharmaceutical Quality System by enabling Strategic Decision Making. The evidence and insights generated y the Data Scientist will be used to inform product and quality system improvement strategies and initiatives, you will also contribute to functional, cross functional, enterprise-wide or external initiatives that shape AstraZeneca's Data & Analytics approaches. This will require a good understanding of the pharmaceutical and healthcare environments, as well as strong scientific and technical data science expertise. You will need strong strategic, collaboration and communication skills, as well as an entrepreneurial mindset, to transform the way we use data and analytics to develop and deliver medicines for our patients, and partner with cross-functional teams and external partners with considerable independence. Key Accountabilities IDENTIFY ADVANCED ANALYTICS NEEDS & RECOMMEND DATA SOLUTIONS: Ask the right scientific questions, understand the data requirements and make recommendations on fit-for-purpose data and analytics solutions. DEVELOP DATA STRATEGY & GAIN ACCESS TO DATA: Develop strategic plans to access internal cross functional data sources to support advanced analytics needs, and gain access to data through collaboration and data generation. DIVE INTO DATA: Develop a comprehensive and deep understanding of the data we work with and foster learning with colleagues using analytical tools and applications to broaden data accessibility and advance our proficiency/efficiency in understanding and using the data appropriately. BE AN EXPERT IN APPLYING METHODS: Stay current with and adopt emergent analytical methodologies, tools and applications to ensure fit-for-purpose and impactful approaches. PRODUCE HIGH QUALITY ANALYSES: Apply rigor in design and analytical methods; plan for data processing; design a fit-for-purpose analysis plan, assess effective ways of presenting and delivering the results to maximize impact and interpretability; ensure compliance with applicable pharma industry regulations and standards. INTERPRET AND SHARE RESULTS: Communicate findings to internal stakeholders, and wider business communities COLLABORATE & SHAPE: Collaborate and contribute to functional, cross-functional, enterprise-wide or external data science communities, networks, collaboratives, initiatives or goals on knowledge-sharing, methodologies, innovations, technology, IT infrastructure, policy-shaping, processes, etc. to enable broader and more effective use of data and analytics to support business. Required Skills & Knowledge Msc, or PhD (desirable) degree in a quantitative discipline Fluency in statistical programming languages (R, Python, etc.) Experience implementing advanced Analytics approaches (machine learning, longitudinal data analysis, etc.) Experience with technologies required to undertake analyses on large data sources or with computationally intensive steps (SQL, parallelization, Hadoop, Spark, etc.) Experience producing interactive outputs (Shiny, etc.) Contributor to open source packages, libraries or functions Experience implementing reproducible Research practices like version control (e.g.,using Git) and literate programming Demonstrated track record of developing and execution of data science projects Demonstrated experience with managing project scope and driving delivery in an evolving environment requiring proactivity and effective problem-solving and prioritization when faced with challenges AstraZeneca is an equal opportunity employer. AstraZeneca will consider all qualified applicants for employment without discrimination on grounds of disability, gender or gender orientation, pregnancy or maternity leave status, race or national or ethnic origin, age, religion or belief, gender identity or re-assignment, marital or civil partnership status, protected veteran status (if applicable) or any other characteristic protected by law.