Scientist, Research Data Modeling - Princeton, NJ

dsm-firmenich•Princeton, NJ

8d•$100,000 - $140,000•Hybrid

About The Position

We are seeking a Scientist, Research Data Modeling to join our Data Science & AI organization, working within the Research Data Domain. In this role, you will design and deliver data models that help transform diverse scientific, experimental, and operational research data into trusted, discoverable, reusable, and AI-ready assets. You will work closely with senior data modelers, scientists, data engineers, data governance specialists, and platform teams to support research data products and use cases across formulation, ingredient discovery, laboratory workflows, experimental data, and other scientific domains. The ideal candidate has a strong foundation in data modeling, good technical understanding of modern data platforms, and an interest in applying data management and modeling practices to complex research environments.

Requirements

PhD, Master’s, or Bachelor’s degree in a scientific, engineering, data, informatics, or related discipline, with relevant experience in research, data modelling, data analysis, engineering, or informatics.
Typically 2+ years of relevant experience for Master’s/Bachelor’s candidates; PhD candidates with relevant research/data experience are also encouraged to apply.
Hands-on experience in conceptual, logical, and/or physical data modelling, including approaches such as dimensional, Data Vault, semantic, or entity-relationship modelling.
Experience or strong interest in research, R&D, laboratory, formulation, nutrition, biotech, pharma, microbiome, omics, or related scientific data environments.
Working knowledge of FAIR principles, metadata, data quality, data governance, stewardship, reusable data products, and modern data platforms/tools such as Databricks, Delta Lake, dbt, SQL, Python, or cloud data platforms.
Strong communication and collaboration skills, with the ability to work across scientists, data engineers, governance teams, and technical stakeholders to turn ambiguous requirements into structured data models.

Responsibilities

Design and maintain conceptual, logical, and/or physical data models for research and scientific data domains.
Work with scientists and business stakeholders to understand data needs, capture requirements, and translate them into practical data structures.
Collaborate with data engineers and platform teams to implement models in Databricks environments, or other enterprise data platforms.
Apply data modelling standards and best practices, including FAIR principles, metadata management, semantic clarity, and reusable data design.
Work in multidisciplinary teams across Science & Research, Data Science & AI, Digital & Technology, and business domains.