AstraZeneca is a leading global biopharmaceutical company, headquartered in Cambridge and with a deep-rooted commitment to the UK. A global pharmaceutical company with a major UK presence. Our purpose is to push the boundaries of science to deliver life-changing medicines. The best way we can help patients is to be science-led and share this passion with the scientific, healthcare and business communities of the UK.

 

AstraZeneca Cambridge, UK
22/10/2018
Full time
Imagine turning your passion for Data Science, Machine Learning and Artificial Intelligence into insights, predictions and actions that could help develop medicines that change lives. Pushing the frontiers of innovation. Collaborating with some of the finest minds in their fields. Working in an environment that stimulates your curiosity, and enables you to develop your expertise and reach your potential. AstraZeneca is a global, science led biopharmaceutical business and our innovative medicines are used by millions of patients worldwide. Artificial Intelligence (AI), including deep learning (computer algorithms inspired by biological neural networks), is revolutionising all stages of drug discovery and development from drug design to pivotal clinical trials and beyond. At AstraZeneca, we are deploying AI across the drug discovery phases to discover new connections between data, provide novel insights, accelerate scientific understanding, and increase productivity.  For example, we are using AI to help design and synthesise new drug molecules, analyse and interpret the vast amount of data from imaging studies, and for biomarker research to match the right drug to the right patient. In our clinical trials, AI is also enabling us to continuously monitor incoming safety data and alert our scientists to safety signals that need attention. As a Data Scientist and AI Graduate you will help our scientists make their data ‘AI-ready’, and integrate the latest AI advances into all our drug development programmes.  Together we will harness the power of our data and AI to deliver life-changing medicines to patients worldwide. What does the programme involve? Your two-year programme begins with an introduction to drug discovery, and how we harness the potential of Data Science, Machine Learning and Artificial Intelligence. You’ll then select two eight-month placements where you’ll apply your developing skills and experience to help us overcome challenges in some of our active research projects. Your placements will help you develop your technical skills and industry knowledge, and on our Global Graduate Development Programme you’ll develop the interpersonal and business skills that will set you up for success.Throughout the programme you’ll work alongside our experts, and take advantage of our close collaborations with leading international academic institutions and organisations to deliver work with real impact. Placements could involve… Applying the latest techniques in machine learning and artificial intelligence to preclinical data from drug projects to help detect and understand safety risks and guide the development of new medicines. Exciting work which could also help us refine, reduce and replace animal experiments. Combining innovative de novo molecule design methodologies utilizing state-of-the-art artificial intelligent technologies with chemistry automation to design novel molecules of therapeutic interest. Working with our Quantitative Clinical Pharmacology Team to optimize clinical development programs using Bayesian statistic and machine learning modelling. Developing Bayesian machine learning models for predicting the mechanism and probability of drug toxicity in liver, heart, and other organs. Incorporating existing knowledge of causal relationships and toxicity mechanisms into the prediction problem, and applying Bayesian models to account for uncertainty to improve predictions. Contributing to challenging projects using advanced Machine Learning models to improve drug discovery by generating testable hypotheses about targets, predicting compound properties, automating complex decision-making processes, and turning data into knowledge. Applying Artificial Intelligence to integrated electronic health records and genomic data to redefine cancers and help improve patient outcomes. Working with our Data & Analytics Team to identify, assess and recommend the most innovative external partners, and high impact AI technologies. Writing algorithms to support next generation natural language processing, and optimising code to enable powerful predictive models to be used across AstraZeneca. As a Data Sciences and AI Graduate you’ll discover how we’re pioneering more open innovation, and actively sharing knowledge and ideas to make the next scientific breakthrough. Essential Qualification and Skills required: Highly numerate with a strong Maths, Physics, Statistics or Computer Science focused Bachelors or Masters level qualification gained in 2018 or 2019. A postgraduate qualification in Data Science, Machine Learning, Artificial Intelligence, Computational Biology, Computational Chemistry, Bioinformatics or Cheminformatics would be advantageous, but is not essential. Innovative thinking, with enthusiasm, energy and drive. Open-minded, and ready to embrace new ideas and different perspectives. Strong critical thinking, planning, organisational and time management skills. Ability to work as part of a collaborative team that focuses unequivocally on patient needs. AstraZeneca is an equal opportunity employer. AstraZeneca will consider all qualified applicants for employment without discrimination on grounds of disability, sex or sexual 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. Please note that our Assessment Centres will be in Gothenburg, Sweden on 26th of April 2019 Applications open: 8th October 2018 Applications close: 27th January 2019 Date Posted:  08-Oct-2018 Closing Date:  27-Jan-2019
AstraZeneca Macclesfield, England, UK
22/10/2018
Freelance
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.
AstraZeneca Cambridge, England, United Kingdom
22/10/2018
Full time
At AstraZeneca we win through science, it’s at the heart of our every success. That science is only possible when we all work together – we’ll always make sure you’re clear about how your role is connected to our wider mission to really show what science can do. At AstraZeneca we’re relentlessly curious about science, and pursue our work with passion. We believe that when we follow the science we can make game-changing discoveries that can transform the lives of patients around the world. Summary: The Real World Evidence Centre of Excellence is a new group being growing within AstraZeneca. AstraZeneca has a pedigree of experience in Real World Evidence, having developed a coherent strategy to develop and internalize rich data assets the group is now amplifying those investments through a Real World Evidence Data Science capability. Data Scientists that are successful in this role will become part of a growing RWE CoE within the Global Medical Affairs organisation, working on challenging problems Data Science can solve for accelerating evidence generation through to insights that could enable AstraZeneca to develop innovative medicines. He/she will be assist the due diligence on new data providers/vendors to advance AZ’s Real World Evidence (RWE) data maturity, help shape the right analytics strategy for a range of Therapeutic Areas and provide informatics support for data acquisitions. The ideal candidate for this role will bring a proven track record of delivering value through the leverage of routinely collected data from healthcare settings to provide health analytics and insights in both Public Health, Pharmaceutical Research and Development and Commercial context. The role provides coaching, task management and support to Programmers/Statistics/Information Scientists, promoting best practice across multiple domains, and/or stakeholder groups. Typical Accountabilities: Provide scientific guidance on the application of Real World Evidence and observational research data to support pharmaceutical development problems Support Pharmacovigilance teams and Epidemiologists extract value from large observational research data Support technical teams provide access to analytical tools and develop visual analytics to enable self-serving applications for end customers Provide clear technical input, options, and direction to strategic decisions on RWE platform and capability build Provide strategic decisions on AZ Medical Evidence and Observational Research external collaborations in the US and other markets Provide technical input, options and directions to strategic decisions made by AZ observational study teams on study design, data partner selection and best practices in RWE data utilization Assist in building a capability that becomes a source of sustained competitive advantage for AZ/MedImmune in identifying, acquiring, integrating and mining diverse RWE data from multiple geographic and healthcare system sources to support evidence generation and real-world studies Assist in the delivery of analytics and the build of analytics pieces as part of an RWE strategy Evaluate and assess strengths and weaknesses of external RWE data sources via interrogation, and potential partners for advancing the data strategy for a given therapeutic area Maintain a strong insight into the capabilities of potential external partners in RWE, especially for US and emerging markets. Education, Qualifications, Skills and Experience Essential: PhD in data science or other advanced degree in life sciences with post doctoral or other training/work in Medical Informatics or related field Expertise in EMR/Health IT, disease registries, and insurance claims databases Expertise in clinical data standards, medical terminologies and controlled vocabularies used in healthcare data and ontologies (ICD9/10/ReadCode) Experience in Statistical Analysis Plan (SAP) generation and execution for observational studies Expertise in data mining approaches within healthcare settings generating insight from routinely collected healthcare data Expertise in methods development and application using statistical languages such as R/Matlab/SAS/SQL/Hadoop/Python Experience in advanced visualisation and visual analytics of routinely collected healthcare data Experience in real-world evidence and familiarity with health economics/epidemiology, and quantitative science such as health outcome modelling Strong track record in delivering leading edge informatics solutions in pharmaceutical R&D and commercial environments Desirable: Experience in supporting pharmacoepidemiology studies with proven track record of advancing approaches with data science A history of patient care or equivalent background of working at a patient care setting that allows the candidate to bring medical perspective into real-world evidence generation and observational studies Demonstrated ability to build long-term relationships with stakeholders at senior levels, understand relevant scientific/business challenges at a deep level and translate into a programme of informatics activities to deliver defined value Ability to lead & manage multi-disciplinary health informatics projects Strong track record of delivering large, cross functional projects Experience working in a global organization and delivering global solutions Key Relationships to reach solutions Internal (To AZ or teams) Observational Research teams Global Medical Affairs Leads Payer Evidence & Planning Product and portfolio strategy Global Medicines Development Medical Evidence Planning Key experts in Informatics/Analytics environments at AZ/MedImmune, and external partner organizations. External AZ RWE existing, prospective or potential partner organizations, Key academic collaborators and thought leaders 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.