Team Lead/Associate Director Statistics and Machine Learning
Team Lead/Associate Director Statistics and Machine Learning, Data Sciences and Quantitative Biology
Location: Cambridge, UK
Competitive salary & excellent 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 Team Lead/Associate Director 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 position, your main responsibilities are to:
- Be responsible for the biological insights and machine learning team and contribute to the departmental strategy to transform drug discovery. This team is primarily focussed on using connected data and information (e.g. knowledge graphs/networks), 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.
- 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 competencies (bioinformatics and image analytics) would be an advantage.
- Expertise in some of the 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)
- Basic understanding of molecular biology, cell biology, human physiology and/or drug discovery.
- Prior experience with people management
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.