Head of Machine Learning, Optimisation, and Decision Making

Key Skills

Aartificial intelligence, Agile methodologies, Analytical and Problem solving, Data science techniques, Deep Learning, Drug Discovery, Leadership Skill, Machine learning techniques, Sales Optimisation

Job Description

About the group

The Centre for Artificial Intelligence (CAI) is a lab focused on applying machine learning and artificial intelligence research to the toughest challenges at AstraZeneca. We are a team of experts from different fields including computer vision and audio, protein design, graph neural networks, natural language processing, and generative AI. We innovate together with our leading biologists, chemists, and clinicians to close the gap between today's challenges and the forefront of machine learning. If you want to have an impact on shaping patients' lives by accelerating new medicines to patients, this may be the position for you! 

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

• MSc/PhD in Computer Science, Mathematics, the Physical Sciences or other relevant subject area
• 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.