Associate Director, Machine Learning Data Scientist, Oncology Data Science

Associate Director, Machine Learning Data Scientist, Oncology Data Science

Location: Barcelona, Spain 

Salary: Competitive with excellent benefits 

The Computational Biology group is growing in Barcelona, and we are looking for new team members to join us at different roles (Sr Scientist/ Associate Director). 

The Computational Biology group embedded in AstraZeneca’s Oncology Data Science department, drives high-impact cancer research projects and delivers data-driven, actionable insights through the application of computational science to clinical and omics data to all areas of AstraZeneca’s Oncology portfolio.  

AstraZeneca is a company that always follows the science and turns ideas into life changing medicines. Oncology Data Science plays a unique role in driving both discovery and translation through leading computational/data driven approaches to all aspects of the drug discovery process. 

What you’ll do 

The Computational Biology team is undergoing expansion to meet the strategic priorities of Oncology Data Science with the vision of building computational oncology programs to pro-actively drive basic science and generate translational portfolio insights across assets and cancer types in Oncology R&D.  

We are looking for an Associate Director of Machine Learning & Computational Biology to contribute to our efforts modelling multiomic data across scales to further our understanding of the biological mechanisms of response and resistance to treatment using a variety of in-house and public multi-omics data sources (in vitro, in vivo, ex vivo, translational & clinical). This is an opportunity to lead and develop our strategic approaches in the cross-sections between computational oncology, single cell biology, discovery sciences and translational oncology in a highly matrixed collaborative environment. The ideal candidate will have proven academic and/or industry experience in machine learning/artificial intelligence (ML/AI) and its applications in computational oncology with an outstanding track record of leading research projects.  

The successful execution of this role will impact the wider AZ oncology community, and our patients through discovery of features of the tumour microenvironment, cell-cell interactions and the transcriptional/genomic features that govern response and resistance to perturbations. The outcome will contribute to advising drug combination and patient selection strategies, discovery of novel oncology targets and deepen our knowledge of tumour-immune co-evolution. To do this you will:

·        Develop AI/ML computational strategies to define novel approaches for analysis of multimodal data. You will be part/lead of a matrixed team of highly qualified computational scientists and individually contribute to deliver integrated ML/AI predictive and explainable models using spatial, single cell, preclinical, ex-vivo and clinical multi’omic and phenotypic data. 

·        Partner closely with leaders across Oncology Data Science, Translational Medicine and Bioscience to establish a translational data science strategy based on use of ML/AI approaches to facilitates back-translation through discovery of novel targets and/or prediction of drug combinations based on tumour-intrinsic and -extrinsic molecular and cellular insights.  

·        Use your expertise in cancer biology/drug discovery to deliver actionable insights that impact the development of the next generation of cancer medicines. 

·        Form effective collaborations with industry and academic leaders in the field, to develop AZ’s IP and/or publish AZ’s work in high impact journals. 

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