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Senior Scientist, Bioinformatics, Astrazeneca
hace 3 meses
We are dedicated to discovering, developing, and delivering innovative, meaningful medicines and healthcare solutions that enrich the lives of patients.
The vision ofAstraZeneca Oncology is to redefine cancer, redefine our solutions to cancer, and restore patients' lives.
The
Early Computational Oncology team works with our oncology drug project teams throughout the research and early development pipeline, from new target discovery to translational medicine and clinical trials.
:
immuno-oncology, DNA damage response, tumour drivers & resistance, epigenetics, cell therapies and antibody-drug conjugates.
Excitingly the team is growing to meet the challenges of pre-clinical and clinical big data generation initiatives in AZ Oncology including DNA sequencing, single-cell and bulk expression, proteomics and CRISPR.
There exists an opportunity for a talented and motivated bioinformatician with strong expertise in cancer genomics to join the group.
The post holder will work with our DNA Damage Response (DDR) project teams to deliver genomic and transcriptomic profiling and support the characterisation of preclinical models and patients from our clinical trials.
In addition you will develop cutting edge genomics methodologies to expand the biology we can capture from our data.Main Duties and Responsibilities:
- You will work as part of our DDR project teams to characterise the genomic landscape of patients on our clinical trials and define novel markers of response & resistance.
- You will work on ctDNA data from patients upon progression in our clinical trials to understand the landscape of resistant mechanisms.
- You will analyse RWE datasets (Tempus, Flatiron) to derive clinically actionable insights and nominate novel biomarkers to support clinical drug projects.
- You will work as part of our DDR project science teams to contribute to the progression of new medicines toward and through the clinic, using data from clinical trials in combination with preclinical models (e.g. our CRISPR/functional genomics screening centre, PDXs, cell line models).
- You will work with external partners to evaluate and develop clinical assays to be used for selection of patients in our clinical trials.
- Generate actionable biological insight from genomic data.
- Discover and develop new molecular target, mechanism and biomarker hypotheses.
- Link multiomic data sets from patients and _in vitro_/_vivo_ models.
- Integrate and interpret proprietary and public data spanning multiple platforms.
- You will proactively engage in knowledge sharing and peer support, including training our bench science community to build expertise in the tools critical to Oncology bioinformatics.
- Build and steer development of small prototype tools for bench scientists to access and visualise project data.
- Collaborate with industry and academia, and utilise external resources, to find the most effective solutions to our drug discovery problems.
- Publish your work in high impact journals.
Minimum requirements:
- Relevant PhD (or equivalent graduate degree plus proven applied experience), combining:
- deep understanding of the genetics/genomics of cancer.
- Expertise in the analysis of genomic data (WES, targeted panels, WGS) covering QC, handling, processing & interpretation.
- Expertise in querying, mining and interpreting cancer variants from public resources (TCGA, MSK-IMPACT).
- Expertise in differential gene expression analysis and pathway enrichment tools.
- Knowledge of statistical principles applicable to cancer biology.
- Knowledge of cancer genetics and/or the DNA damage response.
- R and/or Python programming and visualisation expertise.
- Skilled in effective communication of complex data to a nonexpert.
Desirable:
- Proficiency analysing and interpreting data from multiple 'omic platforms (NGS sequencing, transcriptomic, epigenetic, proteomic etc.).
- Knowledge of databases of clinical interpretation of cancer variants.
- Understanding of the biological systems and signaling involved in DNA damage response.
- 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.
- A thorough understanding of the contribution of bioinformatics to drug discovery.
- Well networked within external bioinformatics and oncology communities.
- Capability of successfully managing multiple simultaneous projects.
- Working effectively within crossdisciplinary science teams, including with functional leaders.
- Experience contri
