Associate Principal Scientist, Data Science (Virtual Screening & Receptor Biology) - Princeton, NJ

Develop and implement accurate ML and protein AI models of receptor-ligand interactions to drive receptor science using deep structural biology knowledge. Perform in silico screening, docking analyses and energy calculations. Lead the development and drive the adoption of best practices and common computational tools by our global community of data scientists and scientists. Build and scientifically lead a dedicated data science team developing and applying methods for virtual screening and receptor structural biology to our knowledge creation and business projects. Stay up to date on the latest advancements in computational biology, protein and molecular AI and modeling approaches and network as well as collaborate with the global scientific community. Apply and drive application of modern software development and machine learning tools and practices in your own daily work, as well as that of your team, including a high level of proficiency in python. Familiarity with effective and responsible use of coding agents is highly preferred. Strongly contribute to building the data science strategy for ingredient discovery, and team up with other data scientists, life science experts and internal science leaders around the globe to drive project success. Develop and implement ML and AI models for predicting and optimizing performance of formulations in diverse applications. Support formula creation and product development tools with Data Science models and data pipelines. Help to translate scientific research findings into practical applications and collaborate with business units to support solutions that address key business needs. Adopt Data Science best practices and support our global community of data scientists, scientists and experts in perfumery and beaty, taste, texture and health segments. Be informed of latest advancements in cheminformatics, formulation design, computational biology, molecular AI and modeling approaches. Apply modern software development and machine learning tools and practices in your daily work, including a high level of proficiency in Python, git, and team co-development workflows. Familiarity with good use of coding agents preferred. Collaborate with other data scientists and chemistry and physics science experts around the globe to contribute to successful project execution. Experience in leadership of R&D projects and ideally also coaching, mentoring or leading R&D teams.