Head of Graph

**What you**’**ll do**

In this role, you will manage several graph/NLP projects simultaneously and a team of researchers and engineers working on the development of machine learning models aimed at delivering the strategic priorities of the Center for AI. More specifically, you will be responsible for an effective execution of the strategy agreed with the Head of Machine Intelligence and progression of projects through different stages (feasibility, discovery, development, and deployment). You will interact closely with stakeholders across the organization and external partners while realizing these goals. You will ensure that teams are adhering to established engineering standards, coding practices, and way of working. The role will also require mentorship of junior AI researchers and creating an environment for their growth and development. You will also be expected to define your own research agenda and collaborate with the Head of Research to drive forward a cohesive program of basic, fundamental, and applied research. The role gives you an opportunity to realize your ideas in products and services used in clinical trials and across the drug development pipeline.

Accountabilities
- Partner closely with business partners to develop carefully engineered graph-based machine learning projects using modern ML methods
- Lead teams of 3-10 machine learning researchers to successful project delivery using modern engineering standards, methodologies, and collaboration tools
- Mentor and develop more junior AI Researchers, including directing basic research
- Continue to actively engage in ML research with and through the broader ML research community. Publish papers at the major ML conferences, contribute technical blogs and forge strategic alliances with academia and other industry
- Remain at the cutting edge of ML research, especially in a chosen subfield (e.g., graph neural networks, NLP, deep learning, active learning, reinforcement learning).
- Contribute ideas and provide creative input to machine learning projects
- Adhere to CAI standards and ways of working

Examples of projects this team works on are modeling the understanding disease and biology with knowledge graphs, identification of underdiagnosed patients, and optimizing drug assets for yet unexplored indications or discovering new targets for existing indications by creating spatial models to traverse our knowledge graphs.

**Essential **for the role**
- PhD degree in computer science, electrical engineering, statistics, applied mathematics, or a related field.
- At least 6 years of experience in developing graph-based machine learning models (a minimum of 2 years industry experience after completion of PhD studies).
- ML Ops experience: model tracking, model governance, multiple models in different production contexts
- End-to-end experience leading collaborative ML/data science projects in an industry setting.

**Desirable for the role**
- A research program demonstrated by 5 or more journal and conference publications in prestigious venues (with at least 3 publications as a leading author). Examples include but are not limited to: NeurIPS, ICML, UAI, AAAI, KDD, ICLR, EMNLP, WWW, JMLR. Leadership in a high impact model deployment (e.g., developed over a period of 2+ years) or 2+ small-to-medium scale deployments.
- 3+ years of experience within the pharmaceutical industry with either extensive knowledge of clinical trial process (all phases) and associated regulatory activities or the discovery pipeline and accompanying investment decisions.
- Deep understanding of drug development and clinical trial process and data (all phases).
- Participation and activity in the scientific community on program committees, editorial boards, organizing workshops
- Demonstrated leadership potential in research and capability in expanding the frontier of addressable problems.
- Track record of collaborating successfully with AI engineering teams to deliver complex machine learning models.

**Why AstraZeneca?**

At AstraZeneca we’re dedicated to being an excellent Place to Work. Where you are empowered to push the boundaries of science and unleash your ambitious spirit. There’s no better place to make a difference to medicine, patients and society. An inclusive culture that champions diversity and collaboration, and always committed to lifelong learning, growth and development. We’re on an exciting journey to pioneer the future of healthcare.

**So, what’s next?**

Are you already imagining yourself joining our team? Are you ready to bring new insights and fresh thinking to the table? Good, because we can’t wait to hear from you

DSAI #DataAI