Summary

Location: London, UK; Dublin, Ireland

About the Role:

Complex data are integral to our work in clinical studies. This position will be part of a newly established AI & Data Engineering team within the Advanced Quantitative Sciences function at Novartis. Our mission is to leverage automation and artificial intelligence to unlock the potential of complex scientific data sources. By accelerating quantitative decision-making in clinical trials through high-quality data assets and tools, we aim to bring innovative medicines to patients faster.

The AI & Automation lead guides and drives the development and deployment of methods for computational automation of tasks for data extraction, preparation, quality assurance, or the analysis of unstructured or high-dimensional data. By collaborating with our quantitative sci-ences community as well as IT and other internal process innovation functions such as SPPO, the artificial intelligence (AI) and automation lead ensures that novel methods for computational automation and AI can be operationalized safely, effectively and where they have the largest impact.

• Develops AI & automation tools & solutions to accelerate work with clinical documents and data.
• Drives development of IT infrastructure to enable Quantitative Sciences to utilize novel methods for automation and AI - work with vendors and Novartis IT to deploy solutions in the Novartis IT ecosystem.
• Provides technical leadership for applied AI and automation projects
• Builds strong collaborative working relationships and communicates effectively with Quantitative Science partners along with clinical teams to promote a greater mindset where associates may leverage new technology safely in an open and transparent manner.
• Plays a lead role in applying AI & automation through consulting, providing guidance on for complex data and unplanned data challenges.
• Propose and implement improvements to existing processes, form a Risk, Quality & Compliance, and ensure compliance with legal and regulatory requirements, as well as data security and privacy best practices.
• Ensure all AI & automation solutions are well-documented.
• Identify and mitigate risks related to AI & automation tools and projects.
• Stays updated with industry trends and advancements, and helps establish and strengthen the link between Novartis and the external AI community through open-source contributions and publications, as well as through external congresses, conferences, and other scientific workshops and meetings.
• Encourage a culture of continuous learning, constructive collaboration, and innovation within the team, and delegate and collaborate on tasks and projects to ensure the team meets deadlines.

• MSc or PhD in Computer Science/Engineering, Data Sciences, Bioinformatics, Biostatistics or any other computational quantitative science
• Minimum of 3-6 years of developing technical solutions for automation or AI.
• Expert in software / AI engineering practices (including versioning, release management, deployment of models, agile & related software tools).
• Strong software development skills in R and Python, deep learning & analytical frameworks such as pytorch, jax, tensorflow.
• Ideally prior experience in life sciences or drug development.
• Expertise in systems programming languages (e.g. C/C++/Rust).
• Strong working knowledge of at least one large-scale data processing technology (e.g. High-performance computing, distributed computing), databases and underlying technology (cloud or on-prem environments, containerization, distributed storage & databases...)
• Strong interpersonal and communication skills (verbal and written) effectively bridging scientific and business needs; experience working in a matrix environment
• Proven record of delivering high-quality results in quantitative sciences and/or a strong scientific publication track record
• Experience in Artificial Intelligence (Ai), Big Data, Data Governance, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Master Data Management.
• Experience in Machine Learning (Ml), Python and R, Statistical Analysis.