Senior Data Scientist

One of the world's leading pharmaceutical companies, our business is focused on providing innovative, effective medicines that make a real difference in important areas of healthcare. At AstraZeneca, innovation is about more than just research. We’re focused on providing medicines that make a real difference in patient care. AstraZeneca is a place where you can drive innovation in small molecule and biological research and development, it’s also a place with a collaborative culture, and flexible working environment, where you’ll feel valued, energised and rewarded for your ideas and contributions.

In our Oncology group we have a bold ambition to provide cures for cancer in every form. We are following the science to understand cancer and all its complexities to discover, develop and deliver life-changing treatments and increase the potential to save the lives of people around the world.

The Role:

We have an exciting opportunity for a computational chemist or cheminformatician with skills in machine learning for drug discovery and structure-based drug design. We are looking for a highly motivated individual who can apply their domain expertise to solve multidimensional problems within drug design.

As a key part of this role, you will work as part of a team to apply Artificial Intelligence and computational chemistry methodologies to progress the AstraZeneca Oncology project portfolio. You will work with cutting edge technology in an open and collaborative environment nurturing novel ideas alongside drug project teams. As part of a global organisation, you will collaborate with colleagues at our other R&D locations in Gothenburg, Sweden and Boston, USA to drive innovation in AI and machine learning. We always follow the science, and you will be encouraged to present and publish your work.

Main Responsibilities:

The role for the successful candidate will involve working across multiple drug discovery projects and teams, with a focus on applying state-of-the art methods, including artificial intelligence / machine learning and biophysical molecular simulations to accelerate the process of finding new drugs to treat cancer. You will have a key role in developing, validating, and applying tools for molecular de novo design and prediction of bioactivities and other properties. You will use structure-based design methods including molecular simulations to enhance these techniques. More specifically you will:

• Impact multiple discovery projects simultaneously by efficiently and pro-actively applying a wide variety of machine learning, cheminformatics workflows & artificial intelligence methods to address project design challenges.
• Implement generative AI models for molecular de novo design on projects, testing new features and helping to guide future capability development with the goal to best impact project design.
• Develop machine learning methods for target activity prediction and ADMET endpoints, including curating appropriate datasets.
• Use and validate molecular simulation and structure-based design methods both in isolation and to supplement and enhance AI/ML de novo design workflows.
• Drive scientific progress in applying machine learning in drug discovery.
• Be able to apply a wide range of computer-aided drug design tools and methodologies and continue to learn new techniques in broader computational chemistry as you gain experience.
• Publish and present your work both internally and externally.

Qualifications:

• A PhD (or equivalent experience) in Chemistry, Computational Chemistry or Cheminformatics.

Essential Requirements:

• A wide knowledge of computational chemistry; cheminformatics and machine learning concepts.
• Expertise in a variety of machine/deep learning algorithms/architectures (e.g. Transformers, RNNs, GNNs, CNNs, Diffusion, SVM, Random Forest, Linear Regression)
• Experience with scripting/programming (e.g. Python, R, C++, Java) and pipelining tools.
• Expertise in de novo design tools, including scoring functions and selection protocols.
• Experience with ligand and structure-based drug design and with appropriate tools, including virtual screening, ligand docking and methods to estimate binding free energy (e.g. FEP).
• A good knowledge of physicochemical properties and DMPK and their importance in medicinal chemistry
• Excellent written and verbal communication skills.
• Excellent communication, presentation, team working and influencing skills.
• Excellent time management abilities .

Desirable:

• Experience in applying computational/Machine Learning methods to medicinal chemistry problems in a drug discovery setting, delivering tangible outcomes.
• Publications in computational chemistry or Machine Learning fields.
• Experience in running and analysing molecular simulations (e.g. molecular dynamics)

Date Posted

Closing Date

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 authorization and employment eligibility verification requirements.