Senior Scientist of Applied Mathematics and Statistics/Senior Scientist of Machine learning and AI, Early Computational Oncology
This role offers the flexibility to be based at either our Waltham, US or Gaithersburg, US locations.
Do you have a strong background in Machine Learning, statistics, applied mathematics or computer science? Are you passionate about bringing data science to patient benefit? If so, this unique opportunity may present your next career move!
We have an exciting opportunity for someone passionate about the power of data and Artificial Intelligence (AI)/stats as a catalyst for change in healthcare.
AstraZeneca
At AstraZeneca, we put patients first and strive to meet their unmet needs worldwide. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality. If you are swift to action, confident to lead, willing to collaborate, and curious about what science can do, then you're our kind of person!
At AstraZeneca, we are united by a common purpose: to push the boundaries of science to deliver life-changing medicines. Every single day, we make a difference by delivering potentially life-changing medicines to millions of people worldwide. A significant investment in innovative data science and AI is at the forefront of our drive to make hearts healthier, help people breathe easier, and ensure more people survive cancer.
Business Area
The Early Computational Oncology group delivers data-driven, useful insights through the application of computational science to all areas of AstraZeneca's Oncology portfolio and has a unique role to play in leading the adoption of computational/data driven approaches to the drug discovery process. The group has substantial expertise and a renowned track record in bioinformatics and data science, undertaking both portfolio applied informatics research and the development of cutting edge capabilities.
Within the Early Computational Oncology group, we have a strong and diverse group of data scientists, algorithm developers and applied mathematicians working together with computational biologists, bioinformaticians and software engineers to deliver the next drug, next biomarker and next insights to the benefit of cancer patients.
The Data Science and AI group in Early Oncology is growing to meet the strategic challenges in AZ Oncology and the huge increase in data availability across preclinical and clinical domains. Advanced analytical techniques and effective presentation skills will be essential to fully harness the high dimensionality and size of these data and translating these to the clinical practice. Recent advances in AI and representation of biological data offer huge promises, capturing prior knowledge and constraints within a machine learning/stats framework to derive smarter and more interpretable predictions.
We are looking for a talented and established scientist with a strong quantitative background in applied math, stats and ML/AI capable to think creatively and able to explain complex mathematical ideas in simple ways. You will work in a dynamic and multi-disciplinary environment on multitude of data rich projects in all stages of the value chain in big pharma. All projects are patient centric - your work will impact patient's life. This is a great opportunity to take a technical leadership role in shaping AZ's oncology portfolio with data science and AI, work in the computational hub of Oncology R&D, and be exposed to a wide spectrum of technologies, data sets, trials, treatments, and drug development projects.
You will focus on leading and developing new algorithms in ML/AI or applied math/stats to enhance our capabilities to inform early and late clinical trials. You will use a wide range of AI platforms and tools we have established in the group as a baseline to bring new methodologies to the table, as well extending and improving them.
Key responsibilities include:
Use ML/AI, bio/statistics and applied math to design and develop new algorithms to solve problems and enhance computational techniques in drug discovery and translational medicine in Oncology (e.g. patient stratification, biomarker discovery, omics AI, target prioritization). Work together with computational biologists, bioinformaticians, engineers and biologists to understand the needs and communicate results and tools to the organization and in conferences and scientific journals. Work in a dynamic and multidisciplinary research environment, adapt quickly to new technologies and methods. Advise on best practices of statistical analysis and applied math techniques.
Essential requirements:
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