As part of AstraZeneca's Data and AI transformation, we have an exciting opportunity to take a leading role in a new team committed to pushing the boundaries of Oncology R&D by building and analyzing disease knowledge graphs. The team will take full advantage of big data initiatives at AstraZeneca, such as investment in a Functional Genomics Center with CRUK, partnership in the MIT Broad Institute's DepMap consortium, membership of the UK Biobank, and next generation sequencing of thousands of patients in our trials. By connecting insight with data to build a fuller understanding of the complexities of cancer biology, this team will drive discovery of a new generation of cancer targets and precision medicines. Join a company where your work will impact patients' lives. No matter what you do, or how small your contribution, know that your work impacts lives. That's what drives our Data Scientists to work here rather than anywhere else.
- A graduate degree (e.g. Masters or PhD) in a quantitative discipline (Systems Biology, Data Science, Engineering & Modeling, Applied Mathematics, Physics or similar).
- Previous extensive relevant work experience, including full time industry experience or post-doctoral research.
- A deep understanding of oncology or drug discovery, and the molecular drivers of human diseases.
- Extensive experience working with pre-clinical or clinical biological data e.g. genomics.
- Experienced project manager:
- Proven ability to mentor and motivate teams, directly and indirectly.
- Comfort supervising work in cross-disciplinary and global project environments.
- An ability to influence leadership and direct scientific and technical strategy.
- Expert analyzing data in network or graph representations, including use of state-of-the-art statistical and machine learning techniques, particularly in one of:
- systems biology, Bayesian networks, quantitative modeling or causal reasoning
- machine/deep learning integrating knowledge with biological/health data
- representational learning of graphs/networks (e.g. GCN's)
- transfer learning, machine attention, active learning, reinforcement learning
- Skilled in effective communication of complex data and methods to a non-expert.
- Programming proficiency esp. Python and R, networked and cloud-based systems, FAIR data practices, and graph data representations e.g. triple/quad stores.
- An excellent publication track record.
- Well networked across relevant data science and scientific communities.
About the job:
As the Lead Data Scientist in the team, you will be motivated to explore non-traditional approaches to find meaning in big data for health. You will apply your skills and innovation to:
- Working alongside domain experts to maximize value to drug project teams.
- You will take a position on the Computational Oncology and cross-functional Knowledge Graph leadership teams to define priorities, project alignment and strategic direction.
- Influence investment in data generation or licensing for knowledge graph projects, via articulation of the vision and impact on the drug R&D pipeline to senior stakeholders.
- You'll coach and supervise a team of 5 bioinformaticians and data scientists to discover novel drug targets and biomarkers:
- Bring a network understanding of oncology into and out of the knowldge graph.
- Guide identification and curation of data underpinning the graph.
- Drive input of assertions to the graph through analysis of published and proprietary biological knowledge and data.
- Interrogate disease knowledge graphs to reveal patterns and actionable insight. impacting research and clinical practices.
- You will direct and collaborate with IT data foundation projects, data and graph engineers, bioinformaticians, and visual analytics experts to create end:end solutions.
- Steer design of machine attention, active learning and reinforcement learning to maximize benefit to and from AZ scientists/clinicians.
- You'll educate the AZ scientific community to recognize opportunities for knowledge graphs and adopt a FAIR data-first culture.
- You will collaborate with the burgeoning Data Science and AI community across AZ to benchmark best practices and maximize impact, sharing code and peer insight.
- Maintain awareness of state-of-the-art applications of knowledge graphs and machine learning for drug discovery and influence strategic direction of the team.
- You'll grow our external reputation by publishing innovative methodologies and scientific discoveries.
Location: Cambridge, UK
Closing Date: 8th December 2019
Apply today to be part of something extraordinary.
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorisation and employment eligibility verification requirements.