Associate Principal Scientist, Bioinformatics, Oncology
At AstraZeneca we turn ideas into life changing medicines. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality.
The vision of AstraZeneca Oncology is to redefine cancer, redefine our solutions to cancer, and restore patients' lives. To achieve this, we have an exciting and diverse portfolio combining agents targeting tumour cell drivers/dependencies and the tumor microenvironment and immune response.
The Oncology Bioinformatics group work within cross-disciplinary drug project teams throughout the discovery and translational pipeline, driving new target discovery, pre-clinical and clinical research. The global group, currently 12 full time positions plus graduates and post-docs, spans the UK and US.
We are generating clinical and pre-clinical multi-omic data sets to advance precision medicine and drive a new generation of cancer targets. An exciting opportunity exists for a talented and motivated individual with expertise in tumour driver biology to lead the bioinformatics efforts in this area of our portfolio, with responsibility for the scientific output of 3 - 5 bioinformaticians. You will work closely with your peers that have responsibility for drug resistance, DNA damage and immune oncology.
- Provide strategic oversight, direction and prioritisation to bioinformatics activities within the tumour drivers portfolio
- Lead the development of new approaches that benefit the identification and characterisation of tumour drivers
- Work closely with bioscience and translational science discipline leaders to understand where bioinformatics approaches can best impact their scientific and technical challenges
- Identify, propose and lead external collaborative interactions to support the tumour drivers portfolio and enhance relevant methodological capabilities
- Design and apply innovative computational/statistical algorithms and visualizations to:
- Generate actionable biological insight from genomic data
- Discover and develop new molecular target, mechanism and biomarker hypotheses for drug projects
- Link multi-omic data sets from patients and in vitro/vivo models
- Integrate and interpret proprietary and public data spanning multiple platforms
- Find new ways of interpreting, modelling or finding meaningful patterns in complex data
- 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 further development of small prototype tools for bench scientists to access and visualize project data
There is a competitive salary and flexible benefits package on offer for this position. Applications close on Friday 17th November 2017.
- Relevant PhD (or equivalent graduate degree plus experience), plus between 3 and 5 years post-doctoral or industrial experience.
- Deep expertise in genetics/genomics, oncology/tumour driver biology and computational biology.
- Proficiency analyzing and interpreting data from multiple 'omic platforms (NGS sequencing, transcriptomic, epigenetic, proteomic etc.).
- Expertise applying mathematical approaches to identify and interpret associations in diverse molecular and phenotypic data; knowledge of large-scale machine learning techniques.
- Understanding of the biological systems and signaling involved in tumour drivers & resistance.
- An enthusiasm to explore non-traditional approaches to bring big data together in biologically meaningful ways.
- Programming in a Unix environment.
- R programming expertise (inc. use of Bioconductor and Shiny).
- Skilled in effective communication of complex data to a non-expert.
- An excellent publication track record.
- Familiarity with drug discovery and the role of bioinformatics therein.
- Well networked within external bioinformatics and oncology communities.
- Experience of leading bioinformatics efforts aligned to drug discovery.
- Effective leadership of collaborative projects involving cross-disciplinary and global teams.
- Experience using data science techniques for:
- Time series and real-time data analytics
- Real world evidence/electronic health records
- NLP and cognitive approaches for consumption and actionable interpretation of scientific literature.
- Predictive machine/deep learning, with a focus on techniques revealing actionable biological insight from feature use and predictive rules.
- Mathematical, Boolean and/or computational modeling of human disease mechanisms.
- Graph and Bayesian modeling techniques.
- Experience with Python/Perl programming languages
- Software development experience including the creation and maintenance of relational databases
Next Steps - Apply today!
AstraZeneca is an equal opportunity employer. AstraZeneca will consider all qualified applicants for employment without discrimination on grounds of disability, sex or sexual orientation, pregnancy or maternity leave status, race or national or ethnic origin, age, religion or belief, gender identity or re-assignment, marital or civil partnership status, protected veteran status (if applicable) or any other characteristic protected by law. AstraZeneca only employs individuals with the right to work in the country/ies where the role is advertised.