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Associate Director, Machine Learning Data Scientist, Oncology Data Science

Ort Barcelona, Katalonien, Spanien Anzeigen-ID R-201374 Veröffentlichungsdatum 17/07/2024

Associate Director, Machine Learning Data Scientist, Oncology Data Science

Location: Barcelona, Spain

Salary: Competitive with excellent benefits

The Computational Biologygroup 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 Sciencedepartment, 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.

Essential requirements for the role

·Relevant PhD in applied ML/AI to cancer biology or computational/systems oncology. 

·Associate Director Level: Experience in developing and using AI/ML methods and leading successful computational biology programs in an academic and/or industry setting. 

·Senior Scientist Level: Experience in applied AI/ML methods and driving computational biology research projects analyzing, integrating and interpreting data from multiple 'omic platforms in an academic and/or industry setting.

·Proven expertise in one of more of areas of AI/ML such as Bayesian modelling, variational methods, multi-instance learning, auto encoders, generative AI, LLMs, ANNs, transfer learning, pretrained/foundation models. 

·Deep knowledge of cancer genomics (including clonality, mutations, evolution) and algorithmic and statistical methods applicable to cancer genomics/proteomics and/or single cell biology.

·Highly attuned communication skills and experience of working in a matrix environment and coordinating efforts and responsibilities around cross-functional project goals.

·Ability to coordinate and pursue simultaneous projects and deliver to deadlines.

·R and/or Python programming expertise in a Unix environment making use of high-performance computing environments.

Desirable requirements for the role:

·Outstanding publication record.

·Experience in ML-based modelling of single cell and/or spatial omics data.

·Well connected to a wide network of bioinformatics and oncology communities.

Does this sound like you? Next Steps – Apply today! 

Closing date for applications is no later than 30th June

To be considered for this exciting opportunity, please complete the application on our website at your earliest convenience – it is the only way that our Recruiter and Hiring Manager can know that you feel qualified for this opportunity.

We reserve the right to close this advertisement early, if we receive sufficient applications for this role. Therefore, if you are interested, we encourage you to submit your application as soon as possible. 

If you know someone who would be a great fit, please share this posting with them.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

10001249 E DAIN

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