Senior Scientist, Computational Oncology, Oncology - Barcelona, España - AstraZeneca

AstraZeneca

Empresa verificada

Barcelona, España

hace 3 semanas

Publicado por:

Isabel García

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Descripción

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

Work in a highly dynamic team of computational biologists and develop new approaches to address challenging biological questions relating to cancer evolution, tumourintrinsic and extrinsic mechanisms of resistance to IO.
Partner closely with scientists across Translational Medicine and Bioscience to facilitate backtranslation through discovery of new targets and/or prediction of drug combinations based on tangible molecular and cellular insights.
Analyse inhouse preclinical, clinical, and RWE datasets to derive clinically actionable insights and nominate novel biomarkers to support clinical drug programs.
Proactively engage in knowledge sharing and peer support, including training bench scientists to build expertise in computational biology tools.
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 the field of computational biology, bioinformatics, systems biology, or oncology data science and experience in either:
bulk and single-cell RNA sequencing technologies.

- cancer genomics/proteomics.

Proficiency analysing and interpreting data from multiple 'omic platforms (NGS sequencing, transcriptomic, epigenetic, proteomic etc.).
Expertise in the analysis of genomic data (WES, targeted panels, WGS) covering QC, handling, processing & interpretation.
Expertise in pathway enrichment tools and interpretation of the data to derive actionable biomarkers.
Expertise in cancer genetics, cancer signalling and/or tumour microenvironment.
Knowledge of statistical methods applicable to cancer biology.
Ability to manage simultaneous projects to tight deadlines.
R and/or Python programming and visualisation expertise.
Skilled in effective communication of complex data to a nonexpert.

Desirable requirements:

Publication record in either cancer genomics/proteomics, bulk or singlecell transcriptomic applied to cancer.
Expertise in the development of novel statistical approaches for the analysis of biological data.
Awareness of machine learning, graph modelling, artificialintelligence, Bayesian analytics or other nontraditional approaches to model biological data
Ability to work effectively across multidisciplinary science teams.
Experience contributing to the research community through publications, conferences and coding expertise.