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 Data Engineering Lead guides and drives execution of the development of robust data assets from both internal and external sources to support data interrogation, driving research and development efforts, and external collaborations. By collaborating with our quantitative science community, TSC and Statistical Programming colleagues, IT, QA, BR data science teams, and vendors, the Data Engineering Lead ensures the delivery of fit-for-purpose and high-quality data assets, automation pipelines and associated technical and quality documentation, facilitating scientific advancements in drug development.

• Develop data pipelines and IT infrastructure solutions to enable Quantitative Sciences to utilize high quality datasets to make quantitative decisions at trial and/or project level activities, pooling structured data from clinical trials, incorporating evidence from multimodal or large sources of data (such as genomics, biomarkers, digital technology, sensors, imaging, and real-world evidence) and producing high-quality versioned data assets and software pipelines for reproducible analysis
• Provides technical leadership for data engineering projects, plan and oversee data engineering projects from conception to deployment, define project scope, goals, and deliverables, ,monitor project progress and adjust as necessary to meet deadlines.
• Build strong collaborative working relationships and communicates effectively with Quantitative Science partners along with clinical teams to promote a greater mindset where associates may leverage each other's skills in an open and transparent manner.
• Play a lead role in agile engineering and consulting, providing guidance on for complex data and unplanned data challenges.
• Focus on Risk, Quality & compliance, proposing and implementing improvements to existing processes, ensuring all data engineering processes are well-documented, as well as ensuring compliance with legal and regulatory requirements, and data security and privacy best practices.
• Stay updated with industry trends and advancements, and help establish and strengthen the link between Novartis and the external data engineering 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, facilitate communication between team members and other departments, 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 4-6 years of developing data pipelines & data infrastructure, ideally within a drug development or life sciences context
• Expert in software / data engineering practices (including versioning, release management, deployment of datasets, agile & related software tools).
• Strong software development skills in R and Python, SQL.
• 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 solid 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.