Lead Data Scientist, Gen-AI Expert

About the role: We are seeking a Lead Data Scientist, Gen-AI Expert to join the Data Science and Generative AI team within Data Analytics & Platform in Novartis China. In this role, you will translate high-value business questions across commercialization, medical affairs, and life sciences into scalable AI/ML and generative AI capabilities, including enterprise knowledge bases, RAG, intelligent search, reusable data pipelines, and production-grade AI engineering standards. This is an opportunity to shape governed, reusable, and business-impactful AI solutions that improve decision quality, operational efficiency, and customer experience across the commercialization organization.

Key Responsibilities：• Responsible for translating business pain points related to commercialization, medical affairs, and life sciences into clear machine learning or generative AI technical pathways, product requirements, and mid- to long-term capability roadmaps.• Deeply engage in commercialization, medical affairs, and life sciences business scenarios; work with business teams to clarify high-value questions; rapidly translate ambiguous needs into executable AI/ML technical solutions; and achieve end-to-end delivery through prototype development, PoC, business validation, and production deployment.• Lead the development of enterprise-level knowledge bases, retrieval-augmented generation (RAG), intelligent search, memory systems, and reusable data pipelines, covering knowledge architecture design, document processing and indexing strategies, metadata and access management, data asset reuse, evaluation set development, quality monitoring, content governance, and continuous operation mechanisms.• Responsible for algorithm prototype design, performance optimization, model evaluation, and capability iteration for generative AI applications. Apply technologies including large language models, prompt engineering, knowledge graphs, recommendation systems, causal inference, deep learning, or multi-agent frameworks, and make engineering trade-offs across accuracy, latency, cost, traceability, and user experience to form stable, scalable, and traceable enterprise-level solutions.• Establish enterprise-level AI engineering and standardization frameworks, including model evaluation metric systems, fine-tuning processes, deployment and monitoring standards, quality tracking, risk control, and compliance assessment approaches, ensuring that AI capabilities operate safely, compliantly, and reliably in pharmaceutical industry scenarios.• Work closely with business, data, engineering, IT, compliance, legal, and external vendor teams to manage cross-functional expectations and delivery cadence; clearly communicate technical value, business impact, and return on investment (ROI) to senior stakeholders; and drive the adoption of AI culture and standardized workflows on the business side.Essential Requirements: • Master’s degree or above, preferably in computer science, artificial intelligence, machine learning, data science, statistics, mathematics, information management, or related disciplines; experience in healthcare, life sciences, pharmaceutical commercialization, consulting, or enterprise-level digital transformation is preferred.• More than 5 years of experience in artificial intelligence, machine learning, generative AI, data science, enterprise-level AI products, or platform engineering; full project experience from algorithm prototyping, performance optimization, and engineering deployment to continuous iteration is preferred.• Solid capabilities in frontier algorithms and models, with familiarity in large language model (LLM) applications and fine-tuning methods, including supervised fine-tuning (SFT), reinforcement learning (RL/RLHF), prompt engineering, and multi-agent collaboration frameworks, as well as an understanding of cross-disciplinary technologies such as knowledge graphs, recommendation systems, causal inference, deep learning, and multimodal AI.• Production-grade AI engineering and standardization capabilities, with familiarity in model evaluation metrics, fine-tuning processes, data pipelines, knowledge base and memory system design, deployment monitoring, quality governance, traceability management, and stability requirements for production systems.• Excellent business understanding and technical translation capabilities, with the ability to decompose complex business problems into executable functional requirements, data requirements, and system solutions; balance technical investment, business value, cost efficiency, and return on investment (ROI); and integrate internal and external data resources and partner ecosystems.• Excellent cross-functional project management, communication, and senior stakeholder influencing skills, with the ability to coordinate business, data, engineering, compliance, legal, and vendor teams; establish standardized processes in complex environments; drive the adoption of AI culture; and continuously enhance the team’s innovation capabilities.