Scientist Machine Learning for Ingredient Discovery - Long Island City, NY

dsm-firmenich•Princeton, NJ

1d•$100,000 - $140,000•Hybrid

About The Position

We are seeking a skilled Data Scientist with deep expertise in developing and applying machine learning models, including LLMs and agents, as well as user-facing apps for small molecule design and pipeline prioritization. The successful candidate will join the Ingredient Modeling team within dsm-firmenich's R&D Data Science unit and work with a talented team of data scientists, chemists, biologists as well as perfumers to transform our internal molecule discovery process, closely integrating AI with chemistry, biotech and the understanding of our senses of olfaction and taste. Our goal: deliver an integrated, state-of-the-art discovery engine for fragrance, taste and other business areas of dsm-firmenich. This role offers the opportunity to work on projects that integrate computational chemistry with receptor biology and advanced data science methodologies – all while working in close collaboration with leading domain experts in the field of discovery chemistry, biotechnology, receptor biology, olfaction, and taste.

Requirements

Ph.D. in Cheminformatics, Computational Chemistry, Computer Science, Artificial Intelligence or Quantitative Finance / Economics.
2-5 years of additional academic or industrial work experience in cheminformatics and computational chemistry, ideally applied to ingredient- drug discovery or the sensory biology field, or alternatively other relevant areas
Scientific backgrounds with experience in Machine Learning for Trading, Quantitative Investing, and Systematic Portfolio Management are also welcome
Record of accomplishments demonstrating high drive and keen entrepreneurial innovation.
Strong proficiency in Python and its data science libraries (NumPy, SciPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow, PyTorch, Keras etc.).
Strong proficiency in state-of-the art cheminformatics toolkits such as RDKit.
Strong understanding of machine learning algorithms and techniques, miulti-objective optimization (such as mixed preference optimization) and experience with (graph) neural networks.
Proficiency in responsible and efficient use of AI coding agents, good code management practices and experience working with CI/CD pipelines.
Hands-on experience with Azure or AWS cloud services is a plus.
Excellent problem-solving skills and the ability to work independently and collaboratively.
Strong communication skills and the ability to convey complex concepts to non-experts.

Nice To Haves

Hands-on experience with Azure or AWS cloud services

Responsibilities

Use data science and AI to drive a fast-paced ingredient discovery pipeline that generates maximum value for dsm-firmenich’s taste, texture and health segments.
Develop and implement machine learning models to support dsm-firmenich’s new ingredient discovery process.
Use Python and its most common libraries (e.g. NumPy, SciPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow, PyTorch, Keras) to build and optimize machine learning models.
Apply diverse neural network architectures and multi-objective optimization algorithms to solve complex ingredient design problems including exploration of chemical space.
Deploy and manage machine learning workflows on Azure and AWS cloud infrastructure.
Stay abreast of the latest advancements in machine learning, neural networks and cheminformatics and apply this knowledge to drive innovation within the team.
Apply modern tools such as git, generative AI and agentic workflows, graph and relational data models, cloud-native computing and API endpoints to do your daily work.
Team up with other data scientists, chemists, biologists and perfumers/flavorists to translate scientific requirements into scalable and user-friendly modeling tools and drive project success.