BACKGROUND

RESPONSIBILITIES

• Work closely with new target and disease/model expert teams across AZ Oncology UK/US.
  
  • Understand scientific goals, and identify / suggest informatics solutions.
  • Deliver impactful Bioinformatics support.
• Conceptualise and support innovative information solutions to enable drug discovery projects to optimally utilise relevant data and support business decisions.
• Work with cross-disciplinary groups to translate new hypotheses into clinic application.
• Work with the bioinformatics group leadership to understand and agree project priorities.
• Proactively engage in knowledge sharing and peer support to build expertise in the tools and techniques critical to Oncology Bioinformatics.
• Work collaboratively with IS specialists to deliver scientific information platforms, database systems, processes, tools and techniques to solve scientific problems.
• Build expertise in and support one or more of the following growth areas within the group:
  
  • Profiling of 'omic (including NGS) data from public and proprietary patient and tumour model samples..
  • Integrative statistical analysis and interpretation of multi-omic data sets in vitro/vivo.
  • Application of network biology techniques to extract biological knowledge from pan-omic molecular data.
  • Reactive toolkit build to enhance access to 'omic data by our bench scientists.
• Act in accordance with all AstraZeneca policies.

REQUIREMENTS

• Plus at least 5 years experience in biomedical/life science, genetics/genomics, oncology or drug discovery related discipline.

• Plus at least a total of 6 years graduate/post-graduate (PhD preferred) training and applied experience in a relevant bioinformatics / genomic data analysis discipline.

PREFERENCES

• Expertise in DNA sequence analysis and interpretation.
• Awareness of NGS technologies, and experience of NGS data interpretation.
• Expertise in statistical analysis of molecular data.
• Experience applying bioinformatics and statistical approaches to integrate multiple molecular data types, and identify associations to a phenotypic class.
• R programming expertise (inc. use of Bioconductor).
• A thorough understanding of the role of signaling pathways and molecular targets in human disease.
• Skilled in effective communication of complex genomic data to a non-expert.
• An excellent publication track record.

• Well networked within external bioinformatics and oncology communities.
• Expertise processing and interpreting next generation sequencing data.
• Expertise in analysis of data from other `omic platforms (gene expression, proteomics etc.).
• Experience of statistical modeling approaches for molecular data analysis and simulation.
• Experience interpreting multi-omic data at the network level to understand interactions and context specificity influencing phenotypic class.
• Experience working in a drug discovery, oncology and/or genomics related environment.
• A thorough understanding of the contribution of bioinformatics to drug discovery.
• Knowledge of signaling pathways in Oncology.
• Experience contributing to projects involving cross-disciplinary and global teams.
• Python/Perl programming expertise.

TERMS

LOCALE

COMPENSATION

HOW TO APPLY

DEADLINE