Computational Biologics Design

Role Description

Do you have expertise in, and passion for data science and AI? Would you like to play a pivotal role that impacts the delivery of novel biologics drugs for oncology, respiratory and cardiovascular diseases in a company that follows the science and turns ideas into life changing medicines? Then AstraZeneca might be for you! 

The Biologics Engineering team is responsible for the discovery and optimisation of biological candidate drugs to support all therapy area drug discovery pipelines and for the development of in-house biologics discovery platforms and novel drug modalities to support future drug discovery efforts. 

Central to this effort are the Biologics Augmented Biologics Design team who are building on existing technologies to advance learning from the large and complex data sets we generate and excitingly the team is growing to meet this challenge.  This role provides the opportunity for a talented and motivated computational structural biologist. 

Typical Accountabilities

• Lead the in silico support on pipeline therapeutic projects in collaboration with AZ stakeholders  

• Work collaboratively with data and drug discovery scientists to establish structural analytics workflows for biologics discovery 

• Contribute to the development of an end-to-end data analysis capability within Biologics Engineering team as part of a cross-functional team 

• Drive innovative structural, generative & machine learning methods for the design and optimisation of biologic therapeutics 

• Participate in strategic external collaborations  

• Demonstrate effective communication to translate complex concepts to non-experts in internal and external scientific meetings 

Must Have

•    PhD in relevant field (e.g. Structural Biology, Computer Science, Bioinformatics, Physics and Mathematics) and 2 years experience in Silico Design
•    Knowledge of computational structural biology and demonstrated application of data analysis methods 
•    Experience with structural modelling platforms (e.g. Schrodinger, Rosetta etc) 
•    Familiarity with antibody discovery & optimisation, protein structures 
•    Skilled in applying generative AI, Machine learning or deep learning to design and optimise proteins 
•    Strong, professional communication skills and excellent attention to detail, capable of developing good working relationships with diverse individuals 
•    Experience working within a team environment 
•    Acts with integrity and does the right thing 

Desireable

• Knowledge of FAIR data principles 

• Experience with the analysis of large structural, sequence and experimental datasets.