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 an 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. The Data Sciences and Quantitative Biology team 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 image and data analysis solutions to high dimensional datasets, and by enhancing AstraZeneca’s ability to prioritise target selection and portfolio projects based on probability of technical success.
Main Duties and Responsibilities
As an Associate Director Statistics and Machine Learning your main responsibilities are to:
Lead the statistics and machine learning discipline and drive the departmental strategy to transform 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 best practice and skills development.
PhD, or equivalent, in statistics, mathematics, computer science, engineering, or the life sciences.
Excellent communication skills, especially in communicating quantitative concepts to biologists.
Broad experience in statistical data analysis, and expertise in areas such as 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 one or more of the core machine learning areas such as: ANNs, SVMs, Markov models, Gaussian processes, reinforcement learning, decision theory, probabilistic rule-based learning.
Experience with relevant software tools such as R, Python, or Julia as well as relevant machine learning frameworks such as scikit-learn and Tensorflow.
Basic understanding of molecular biology, cell biology, and human physiology.
Prior experience with people management
If you are interested, apply now!
For more information about the position please contact: Claus Bendtsen (firstname.lastname@example.org).
Date job posted: 28th May 2019
Application to be received no later than: 28th June 2019