Summary
At AstraZeneca we believe in the potential of our people and you’ll develop beyond what you thought possible. We make the most of your skills and passion by actively supporting you to see what you can achieve no matter where you start with us.
We have recently launched a Data Science and Artificial Intelligence (AI) transformation programme to position us at the forefront of our field over the coming years. This includes significant investment in groundbreaking machine learning and automation techniques, supported by an uplift in our data quality, standards and data engineering. We will:
- Advance our capabilities in Augmented Drug Design (ADD) for both small & large molecules – employing the very latest in machine learning and automation approaches to our Design-Make-Test-Analyse (DMTA) and biologics screening processes – leading to increased efficiency in lead identification, identification of novel molecules and higher clinical success rates
- Transform our ability to surface key Biological Insights through novel hypothesis generation algorithms deployed over complex knowledge graphs – leading to enhanced validation of our drug targets as well as the identification of new drug targets, and improving our understanding of the biological mechanisms underpinning disease
- Establish a robust Data Foundation for Science, ensuring our R&D data is F.A.I.R: Findable, Accessible, Interoperable & Reusable. The Data Foundation will implement common technologies, infrastructure & tools, along with the processes, standards & quality assurance – to make data “analytics & AI ready”. In scope is data from omics, clinical trials, ADD, imaging, literature, and real-world data, with more to be added over time
Help us to realize the unprecedented opportunities afforded by advances in data availability, computing power and AI to improve the way we discover and develop medicines, and to make a meaningful difference to patients’ lives.
Are you an experienced Data Scientist looking for an exciting new challenge? Then why not join our Quantitative Biology team in Cambridge, UK and develop and apply computational solutions to help us evolve and extend our capabilities in graph-based data and information modelling and analysis, to support our dedication to find medicines of the future. We are further investing globally in capabilities in knowledge graphs and network-based biology, and you will join a multi-disciplinary team of data scientists and researchers driving us forward in this area with the purpose of providing data-driven insights into biology for discovery of new therapies. Your role will be to work as part of a global multi-disciplinary team to develop and deploy a range of analytical approaches to graph/network data, to derive insights to aid drug discovery. We are looking for multiple individuals with different levels of experience.
The role:
As a Senior or Associate Principle Data Scientist your main responsibilities will involve:
- Collaborating effectively with experimentalists, bioinformaticians, statisticians and other data scientists to develop and deploy algorithms and analytical workflows for deriving insights from graph/network biological research data.
- Contribute to, and where necessary prototype and build, software and analytical pipelines for running graph/network analytics and communicating results to researchers, or allowing interactive approaches
- Keep pace with a rapidly evolving research area, ensuring we can exploit available analytical approaches and where necessary adapt them, or develop other approaches.
- Work with collaborators within and outside the company to share standard methodology, benchmark methods and develop and test new analytical approaches.
- Ensuring scientific excellence in software, processes and results as well as being efficient and well documented
Minimum experience:
- PhD, or equivalent experience, in bioinformatics, mathematics, computer science, statistics, engineering or the life sciences
- Demonstrable experience in one or more of the core competency areas: graph theory based network analysis, representational learning of graphs/networks (inc. neural network based methods), logical reasoning applied to graphs/networks, large scale Bayesian networks, causal reasoning applied to graph/networks.
- Experience with relevant computational tools such as Python, Shell, C/C++, R/R Shiny, SQL/NoSQL databasing, networked and cloud based systems, software versioning tools (e.g. Git/Github).
- Experience in biological research concepts, and data and information types (e.g. biological relationships, NextGen sequencing, genetics data)
- Ability to work effectively in global multi-disciplinary teams
- Good communication skills, with technical and non-technical collaborators
- Experience in drug discovery and disease research would be an advantage
Location: Cambridge, UK.
Salary: Competitive + benefits
Closing date: Wednesday 17th April 2019
If you are interested, please apply.
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.
