Associate Director, Early Data Science and

AstraZeneca is a global, science-driven biopharmaceutical company. We are dedicated to discovering, developing, and delivering innovative, meaningful medicines and healthcare solutions that enrich the lives of patients. The vision of **AstraZeneca Oncology** is to redefine cancer, redefine our solutions to cancer, and restore patients' lives.

The **Early **Data Science** **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. Our global group aligns its activities to AstraZeneca’s primary areas of focus in Oncology**: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 single-cell sequencing and/or cancer genomics to join the group. The post holder will work with our Tumour Drivers and Resistance (TDR) and DNA Damage Response (DDR) project teams to deliver multi-omic profiling and support the characterisation of preclinical models and patients from our clinical trials. In addition, you will develop cutting-edge transcriptomics and genomics methodologies to expand the biology we can capture from our data.

Main Duties and Responsibilities:

- You will leverage multi-omic data (scRNA, scATAC, WES, bulk RNAseq, proteomics) from preclinical models and patients to understand mechanism of action of our compounds in the TDR and DDR areas.
- You will work as part of our TDR/DDR project teams to define novel markers of response & resistance from preclinical models and patients on our clinical trials.
- You will work with external partners to evaluate and develop novel single-cell methodologies to enhance the mechanistic understanding of our compounds.- Generate actionable biological insight from genomic data.
- Discover and develop new molecular target, mechanism and biomarker hypotheses.
- Link multi-omic 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.

Essential requirements:

- PhD and significant postdoctoral experience in either:

- bulk and single-cell RNA sequencing technologies.
- cancer genomics.
- 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 querying, mining and interpreting cancer variants from public resources (TCGA, MSK-IMPACT).
- Expertise in pathway enrichment tools and interpretation of the data to derive actionable biomarkers.
- Knowledge of statistical methods 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 non-expert.

**Desirable** **r**equirements**:

- Publication record in either cancer genomics, bulk or single-cell transcriptomic applied to cancer.
- Knowledge of databases of clinical interpretation of cancer variants.
- Biological understanding of 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, artificial-intelligence, Bayesian analytics or other non-traditional 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 cross-disciplinary science teams, including with functional leaders.
- Experience contributing to the research community through publication, conferences and code.