Teams at Novartis

Lead scientific innovation applying AI and machine learning to biologics, antibody discovery, protein engineering, and protein design. Translate drug-discovery challenges into validated AI opportunities, evaluate emerging models and datasets, lead computational proof-of-concept studies with experimental validation, and guide successful innovations into reusable capabilities. Build external collaborations, influence scientific strategy, communicate evidence and risks to senior stakeholders, and mentor scientists and data scientists.

Design, train, evaluate, optimize, and deploy advanced machine learning models for drug discovery. Own the full model lifecycle, build scalable inference and MLOps pipelines, establish reproducibility and testing standards, validate models with scientific experts, and mentor teams. The role focuses on deep learning, graph-based architectures, foundation models, distributed training, model monitoring, and production performance across latency, throughput, cost, and reliability requirements.

Supports the strategy, development, launch, scaling, and continuous improvement of AI-powered products. Translates product vision into roadmaps, manages product lifecycles, prioritizes features, tracks budgets and resources, and evaluates technical, adoption, and value metrics. The role coordinates cross-functional teams, business owners, DDIT, and external partners while ensuring stakeholder alignment, user satisfaction, ethical AI deployment, and successful transition to maintenance.