AI & Data Engineer

Band

Level 4

 

Job Description Summary

Location: London, United Kingdom #LI-Hybrid

Internal job title: Senior Expert Data Science / Data & AI Engineer

About the Role:

Complex data are integral to our work in clinical studies. This position is 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 & AI Engineer actively contributes to the development and implementation of computational automation methods for tasks such as data extraction, preparation, quality assurance, as well as the AI-assisted analysis of unstructured or high-dimensional data. Working closely with colleagues from quantitative sciences, IT, and internal innovation teams, this position focuses on hands-on delivery and integration of practical automation and implementation of AI solutions & methods through strong engineering practices. The engineer helps operationalize newly developed methods, ensuring their safe and effective application to drive measurable impact in our drug development day-to-day work.

 

Job Description

Key Responsibilities:  

• Develops AI / automation tools and solutions to accelerate work with documents and data from clinical trials. Delivers technical excellence by designing and implementing technology aspects of applied AI and automation projects, from concept to deployment. Contributes to defining the scope, goals, and deliverables of these initiatives, with a strong focus on creating solutions that offer measurable improvements over existing methods.

• Builds and maintains collaborative working relationships, communicating effectively with partners in the Advanced Quantitative Sciences function and clinical teams to foster a culture where new technology can be leveraged in a safe, responsible and transparent manner.

• Drives the adoption of AI and automation by offering expert consulting and guidance on complex and unexpected data challenges.

• Understands risk, quality, and compliance in a drug development setting, ensuring all work aligns with existing processes; maintains well-documented solutions; ensures compliance with legal and regulatory requirements as well as data security and privacy best practices; and proactively identifies and mitigates risks associated with AI and automation tools and projects.

• Remains current with industry trends and advancements, helping to strengthen the connection between Novartis and the external AI community through open-source contributions, publications, and participation in external congresses, conferences, and scientific workshops.

• Fosters a culture of continuous learning, constructive collaboration, and innovation within the team, collaborating on tasks and projects to ensure deadlines are met.

Essential Requirements:

• MSc or PhD in Computer Science/Engineering, Data Sciences, Artificial Intelligence / Machine learning, Bioinformatics, Biostatistics or any other computational quantitative science

• Minimum of 2-5 years of developing technical solutions for automation or AI.

• Prior exposure to work in life sciences or drug development is highly beneficial.

• Expert in software / AI engineering practices (including versioning, release management, deployment of models, agile & related software tools).

• Strong software development skills in R (including package development, deployment, advanced programming) and Python.

• Specialist expertise in one or more of the following: Deep learning & analytical frameworks such as pytorch, jax, tensorflow; LLM / LMM applications & agentic AI; bioinformatics & data science.

• Expertise in systems programming languages (e.g. C/C++/Rust) is optional but can be beneficial.

• 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 (fluent in English, 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

Commitment to Diversity & Inclusion:

We are committed to building an outstanding, inclusive work environment and diverse teams representative of the patients and communities we serve.

Why Novartis?
Our purpose is to reimagine medicine to improve and extend people’s lives and our vision is to become the most valued and trusted medicines company in the world. How can we achieve this? With our people. It is our associates that drive us each day to reach our ambitions. Be a part of this mission and join us! Learn more here: https://www.novartis.com/about/strategy/people-and-culture

Join our Novartis Network: If this role is not suitable to your experience or career goals but you wish to stay connected to learn more about Novartis and our career opportunities, join the Novartis Network here: https://talentnetwork.novartis.com/network

Accessibility and accommodation:
Novartis is committed to working with and providing reasonable accommodation to all individuals. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the recruitment process, or in order to receive more detailed information about the essential functions of a position, please send an e-mail to and let us know the nature of your request and your contact information. Please include the job requisition number in your message.

 

Skills Desired

Apache Hadoop, Applied Mathematics, Big Data, Curiosity, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Deep Learning, Machine Learning (Ml), Machine Learning Algorithms, Master Data Management, Proteomics, Python (Programming Language), R (Programming Language), Statistical Modeling