Lead, Data Scientist
International Flavors & Fragrances
- Location
- Shanghai IBP, China
- Work model
- On-Site
- Level
- Senior
- Posted
- Sep 4, 2026
Skills
About this role
Job Summary
The Lead Data Scientist will lead the design, development, localization, and production deployment of advanced machine learning, generative AI, and agentic solutions for IFF’s Scent business. Working across R&D, formulation, and other business domains, this role will translate complex scientific and business needs into robust models, reusable components, and intuitive AI-enabled applications. The position will adapt globally developed internal AI capabilities for the Chinese market as well as rapidly evaluate and applying state-of-the-art methods to IFF data and use cases. As a data scientist first, the successful candidate must have a strong foundation in Statistics, ML, and AI and preferably have a quantitative STEM background with sufficient scientific fluency to support chemistry-related work, and the technical leadership to collaborate with scientific specialists, guide junior data scientists, and deliver independently within a global, cross-functional organization. IFF is committed to attracting, retaining, and engaging high-performing employees. We are committed to ensuring that all positions within IFF are filled with the right talent and, where appropriate, this objective is achieved by developing current employees. At IFF, we encourage internal movement and career development both within functions and cross functions. We work to ensure the most positive experience throughout the application, hiring and transition process for all those involved, the candidate as well as their current manager and the hiring manager.
Job Summary
Looking for a role that challenges you while making an impact on products people use every day? We are seeking a highly skilled Lead Data Scientist to develop and deliver advanced AI and machine learning solutions for the Scent business. In this role, you will translate scientific and business needs into production-ready models and AI-enabled applications, rapidly applying state-of-the-art methods to IFF problems and data. You will be a key driver of our data science delivery engine, accelerating innovation across R&D, formulation, and other business domains. IFF is a global leader in flavors, fragrances, food ingredients and health & biosciences. We deliver sustainable innovations that elevate everyday products. Scent: Harnessing the full emotional power of scent, driven by pioneering creativity, science, consumer expertise and a mindful approach to fragrance design. The role is based in Shanghai (agile role), China. Be part of a motivated, passionate, and open-minded team where together we can achieve greatness and make a real impact. Your potential is our inspiration. Where You’ll Make a Difference In this role, you will play a critical role in transforming how scientific and business work is performed by embedding AI and machine learning into decision-making and execution. You will lead the translation of complex problems into robust models, reusable components, and intuitive AI-enabled applications as well as help build scalable and differentiated data science capability for IFF. Key Responsibilities & Accountabilities Lead the end-to-end design, development, evaluation, and production delivery of advanced machine learning, generative AI, and agentic solutions, translating scientific and business problems into robust, reusable capabilities rather than one-off analyses. Rapidly replicate, evaluate, and apply state-of-the-art published methods and models to IFF data and use cases; establish reproducible pipelines, evaluation harnesses, and clear performance criteria to maintain scientific rigor, delivery speed, and technical quality. Collaborate closely with the AI Chemist Lead on R&D chemistry and molecular discovery as well as adapt AI applications in the Creative and Consumer domains to the Chinese market. What Makes You the Right Fit PhD or master’s degree in data science, computer science, machine learning, statistics, engineering, chemistry, physics, computational