Principal Scientist Statistics & Machine Learning
Principle Scientist Statistics and Machine Learning, Data Sciences and Quantitative Biology
Location: Cambridge, UK
Excellent salary & company benefits
Are you passionate about statistics and machine learning and applying these to transform drug discovery? Would you like an exciting new challenge in a company that follows the science and turn ideas into life changing medicines? Then why not join our Data Sciences and Quantitative Biology department in Cambridge, UK!
We are now recruiting a Principal Scientist Statistics and Machine Learning to join the Discovery Sciences organisation in Cambridge, UK.
Discovery Sciences is part of AstraZeneca’s R&D unit that delivers candidate drugs into late-stage clinical development. Discovery Science functions across the therapeutic areas to support projects in the drug discovery pipeline from target discovery all the way to clinical candidates. This is a multi-disciplinary team of data scientists with the purpose of providing quantitative insights to biology. We do so by improving the biological understanding of the target and its engagement, including supporting the identification of molecular mechanisms of action, providing data analysis solutions to high dimensional datasets, and by improving AstraZeneca’s ability to prioritise target selection and portfolio projects based on probability of technical success.
Main Duties and Responsibilities
In this role, your main responsibilities are to:
- Work with your colleagues to develop and drive scientific excellence in statistical and machine learning, particularly in the use of connected data and information (knowledge graphs/networks). Your main role will be working as part of a team primarily focussed on demonstrating connected data and information, combined with machine learning and probabilistic approaches (primarily Bayesian), to support decision making in drug discovery.
- Collaborate with drug discovery projects, therapeutic areas, and platform teams to identify and deliver machine learning and statistical modelling solutions to address key drug discovery questions.
- Develop or internalise appropriate algorithms, techniques, and datasets to answer defined biological questions.
- As a recognised technical specialist in your field, you will establish and nurture academic collaborations to access and drive the forefronts of statistics and machine learning science.
- Ensure that results are scientifically robust and documented.
- Lead and develop less experienced staff in standard methodology and skills development.
- PhD, or equivalent, in statistics, mathematics, computer science, engineering, or another quantitative science.
- Excellent communication skills, especially in communicating quantitative concepts to other subject areas (e.g. biologists).
- Broad experience in statistical data analysis, and expertise in experimental design, linear/nonlinear models, mixed effect models, data mining, Bayesian methods, and statistical learning. Additionally, some knowledge of one or more of the departments other core proficiencies (bioinformatics and image analytics) would be an advantage.
- Expertise in core machine learning areas such as: representation learning (graphs), Bayesian machine learning, ANNs, SVMs, Markov models, Gaussian processes, reinforcement learning, decision theory, probabilistic rule-based learning.
- Experience with relevant software tools such as R, Python, Stan or Julia as well as relevant machine learning frameworks such as scikit-learn and Tensorflow, and databasing technologies (SQL and NoSQL).
- Specific expertise in inference from connected data and information (knowledge graphs/networks)
- Proven record of publishing high quality science in the areas mentioned above.
- Experience in establishing and maturing scientific collaborations.
- Basic understanding of molecular biology, cell biology, human physiology and/or drug discovery.
If you are interested, apply now!
For more information about the position please contact: Ian Barrett (email@example.com).
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.