Director of Data Science
At AstraZeneca, we work together to deliver innovative medicines to patients across global boundaries. We make an impact and find solutions to challenges. We do this with integrity, even in the most difficult situations, because we are committed to doing the right thing.
The Digital Health Oncology R&D Human-centered AI and Machine Learning Team strives to transform the patient experience and clinical trial process. We will do so by deploying digital solutions to clinical trials and in the real world to decrease patient burden. The approach the team takes will incorporate clinical trial data, Real World Evidence (RWE) data, clinical free text, medical imaging, Patient Reported Outcomes (PROs), and device data to define new digital approaches to addressing the pressing problems across the AZ R&D portfolio.
The team is looking for a Director of Data Science to specialize in development of innovative machine learning methods passionate about multi-modal datasets including clinical trial data, RWE, imaging data, and other biomedical data sources to address patient burden. This role will sit within the Digital Therapeutics, Diagnostics and Endpoints team with an emphasis on interacting with the Therapeutic Areas, clinical trial teams and other partners in the product development cycle.
This Data Scientist will also work closely with the Digital Health R&D subject matter expert and collaborators to develop novel approaches that support the development of patient and HCP-facing digital products. Applicants should have a foundation in statistics, experience with machine learning in a production environment, detailed knowledge of the clinical trials space in the pharmaceutical industry and experience maintaining a portfolio of machine-learning enabled products.
Examples of projects the team works on include machine learning models for developing digital biomarkers, digital therapeutics, computer vision diagnostics and clinical decision support tools, approaches to quantitatively analyze wearable data, linking of medical imaging data with ‘omics and longitudinal outcomes to identify and/or validate new drug targets, and much more!
Provides sophisticated data science expertise to AstraZeneca projects and recommends data science solutions.
Delivers innovative data science solutions to AstraZeneca projects, appropriately communicating with non-technical collaborators.
Design review with non-technical collaborators and interpretation of sophisticated business needs into technical solutions.
Lead code reviews with teams to ensure quality delivery.
Works within established frameworks to deliver a variety of tasks that support projects in meeting their objectives.
Independently keeps own knowledge up to date and learns from senior team members, proposing training courses for personal development.
Reviews working practices and ensures non-compliant processes are raised.
Career mentor of junior data scientists, represents team at internal and external venues and ensures both development of internal resources and recruitment of external talent.
Collaborate in a multidisciplinary environment with world leading clinicians, data scientists, biological experts, statisticians and IT professionals.
Bachelor’s degree with 5+ years of analytics in a regulated industry setting, must have experience leading multiple projects independently
Demonstrated an outstanding track-record of delivering a portfolio in a highly regulated clinical setting
Practical software development skills in standard data science tools: Python, Agile, Code versioning (bitbucket/git), UNIX skills, familiarity working in cloud environment (AWS preferred)
End-to-end experience leading collaborative data science projects in an industry setting
ML Ops experience: model tracking, model governance, multiple models in different production contexts
Experience developing machine learning first products including time series analysis, forecasting, optimization
Knowledge of range of mathematical and statistical modeling techniques and strive to continue to learn and develop these skills.
Communication, business analysis, and consultancy; ability to present compelling cases to collaborators and operate dynamically to determine solutions
Advance degree in rigorous quantitative science (such as mathematics, computer science, engineering) or M.B.A. with analytics experience in industry.
Experience within the pharmaceutical industry with extensive knowledge of clinical trial process (all phases) and associated regulatory activities.
Sophisticated machine learning models: transformer-based NLP models, reinforcement learning, machine learning models for optimization, GNNs, innovative time-series & forecasting models
devOps/sysOps skills including Kubernetes and experience leading infrastructure as code in CI/CD pipelines
At AstraZeneca we’re dedicated to being an excellent Place to Work. Where you are empowered to push the boundaries of science and unleash your ambitious spirit. There’s no better place to make a difference to medicine, patients and society. An inclusive culture that champions diversity and collaboration, and always committed to lifelong learning, growth and development. We’re on an exciting journey to pioneer the future of healthcare.
So, what’s next?
Are you already imagining yourself joining our team? Good, because we can’t wait to hear from you.
Are you ready to bring new insights and fresh thinking to the table? Brilliant! We have one seat available, and we hope it’s yours.
Where can I find out more?
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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.