Data Science Intern

Key Skills

Aartificial intelligence, Data science techniques, Machine learning techniques

Job Description

10–12-week development experience running through the summer months each year

Internships are meant to be a catalyst in helping students gain hands on learning & exposure to the career they’re pursuing.

Internships benefit students by supporting them to:

• Build their professional network.
• Gain relevant experience for post-graduation opportunities.
• Expose them to AstraZeneca as a possible future employer.

Intern Eligibility Requirements:

• Students must be 18 years or older at time of hire; and not require sponsorship to work in the UK
• Students pursuing undergraduate, masters or doctoral degree.

A salary will be paid.

Knowledge Graphs (KGs) power many projects in AstraZeneca. The KG framework treats drug discovery as a link prediction problem. Nodes representing compounds have edges connecting them to the conditions they target. Discovering new indications for existing compounds is performed by filling in the missing “treat” edges in the knowledge graph. Graph Neural Networks (GNNs) are the backbone of many graph ML applications including ML on knowledge graphs. Research on GNNs is flourishing, and the number of publications proposing new and improved GNN architectures is exploding.

The project will create a benchmark based on Hetionet to establish the accuracy of link prediction with GNNs. In addition, it will leverage data centric statistical techniques to quantify the impact of data interventions on the different methods. The goal is to identify models and data interventions that perform well in these benchmarks in terms of accuracy and robustness. As a stretch goal, this methodology can be applied to other open KGs, or link prediction tasks such as PharMeBINet, or modelling drug-drug interactions.

The project could indicate ways to enhance our internal use of GNNs and will impact on the deliverables of Graph AI for Drug Development strategic project, particularly in drug discovery and understanding genetic pathways, using BIKG and other internal data sources. Hetionet forms the backbone of some of our internal knowledge graphs, and building an understanding of how to improve the models that rely on it can have substantial downstream effects. Moreover, it is the de facto benchmark dataset in this and complementary domains, allowing to compare our performance with other methods in literature.

What are we looking for?

At AstraZeneca, we put patients first and strive to meet their unmet needs worldwide. We’re deeply committed to our strategy of being a great place to work. For us, that includes fostering an environment where everyone can be among inclusive, supportive individuals with curious minds.

A company that genuinely follows the science and values individuals at all levels, you’ll be backed and encouraged to speak up, ask questions, and share ideas to push the boundaries of science and continuously learn and explore.

Our purpose is bold and so is our approach. Becoming a more agile and innovative company means building a dynamic, inspiring culture where we celebrate entrepreneurial thinking and act with a sense of urgency. We are courageous, taking risks and learning from both success and failure. We are curious, creative, and open to new ideas and ways of working. Above all, we are passionate about science and driven to always put patients first. When you join us, you will be part of a great place to work; in an environment that energizes and empowers each of us to achieve our goal to develop and deliver medicines.