Head of Machine Learning, Optimisation, and

**Head of Machine Learning, Optimisation, and Decision Making**

**Location - Barcelona**

**Diverse Minds. Bold Disruptors. Meaningful Impact.**

AstraZeneca is a global, innovation-driven BioPharmaceutical business that focuses on the discovery, development and commercialisation of prescription medicines for some of the world’s most serious disease. But we’re more than one of the world’s leading pharmaceutical companies.

At AstraZeneca we’re dedicated to being a Great Place to Work. Where you are empowered to push the boundaries of science and ignite your entrepreneurial spirit. There’s no better place to make a difference to medicine, patients and society. An inclusive culture that champions diversity and collaboration. Always committed to lifelong learning, growth and development.

**About the group**

**What you’ll do**

We are seeking a highly skilled and innovative Head of Machine Learning, Optimisation, and Decision Making to lead our ground-breaking research and development team at AstraZeneca. You will be responsible for supervising a team of dedicated researchers and scientists focused on large molecule (protein) engineering, graph neural networks, natural language processing, generative AI, and active/reinforcement learning. Your expertise in these areas, combined with your strong leadership skills, will drive the advancement of our research initiatives, and contribute to the development of novel drug discovery approaches.

**Responsibilities**:

- Drive the development and implementation of ground-breaking representation learning techniques using transformer models, active learning, medical knowledge graphs, and other relevant methodologies.
- Collaborate with multi-functional teams to identify research opportunities, formulate strategies, and translate business requirements into innovative representation learning projects.
- Stay up to date with the latest advancements in representation learning, deep learning, and artificial intelligence, and provide mentorship on their applicability to drug discovery and development.
- Foster a culture of continuous learning and knowledge sharing within the team, ensuring the acquisition and dissemination of expertise.
- Manage and prioritise effectively, including budget planning, equipment acquisition, and staffing requirements.
- Collaborate with external partners, academic institutions, and industry experts to use external expertise, stay informed about emerging trends, and explore potential collaborations.
- Ensure compliance with regulatory guidelines and ethical standards relevant to representation learning research.
- Contribute to the company's intellectual property portfolio by generating patents and publishing research findings in reputable scientific journals and conferences.
- Mentor and develop team members, providing guidance, performance evaluations, and career development opportunities.

**Essential requirements**
- Experience of senior data science / AI leadership in a diverse matrix organisation
- Experience of pharma and the life sciences, with a comprehensive understanding of the R&D drug development lifecycle
- Track record of significant data science project, service and skills delivery with demonstration of clear scientific and/or operational value
- Track record of the delivery of AI / machine learning solutions that transform the way business or science is done.
- Proven leadership, promoting motivation and empowerment to accomplish objectives.
- Positive organisational change skills and pedigree
- Strong analytical and problem-solving ability, working in situations with very high complexity.
- Superb communication skills and ability to build positive relationships, resolving issues across a broad customer base.
- Travel - willingness and ability to travel domestically & internationally.

**Desirable**
- Recognized globally as an expert in at least one relevant field.
- Substantial managerial experience.
- Broad experience in improvement projects, and large-scale technology tools/solutions.
- Demonstrated productive collaborative research projects.

**So, what’s next?