Pre Clinical Safety Data Scientist

Kenvue•Summit, NJ

2d•Hybrid

About The Position

Kenvue is currently recruiting for a Pre Clinical Safety Data Scientist. In this role, you will leverage advanced computational, omics, and data science approaches to support pre-clinical safety and product development decisions, integrating AI and machine learning tools to accelerate insights and enhance scientific workflows. You will apply modeling, simulation, and predictive analytics to guide candidate selection, risk assessment, and formulation strategies, while generating and validating hypotheses using internal and external data. Working closely with cross-functional partners across R&D, Medical Safety, and Regulatory, you will translate complex data into clear, actionable insights that inform strategy and innovation. This is a 2–3 year assignment.

Requirements

Candidates must be legally authorized to work in the U.S. and must not require sponsorship for employment visa status now or in the future (e.g. H1-B status)
M.S. or Ph.D. (preferred) in Data Sciences, Computational & Integrative Sciences or equivalent, with proven track record (e.g publications, posters, presentations) of applying modeling, simulation, and computational approaches for real world academic or industry toxicology/health sciences studies
You’re available to complete a 2-year assignment, with the potential to extend to 3 years in Summit, NJ (Hybrid)
Strong proficiency with programing languages such as SQL, Python, R, etc.
Experience and the ability to review relevant scientific literature.
Ability to build effective working relationships.
Strong aptitude for sharing expertise with cross-functional and global teams.
Ability to communicate effectively both orally and in writing in an interdisciplinary environment.
Strong teamwork skills with independent working style, high level of initiative, reliability, and readiness to take on responsibility.

Responsibilities

Designs and implements innovative omics‑based and computational toxicology approaches to address key challenges in product development.
Incorporates AI tools, large language models (LLMs), and agentic workflows into daily scientific operations to accelerate discovery, documentation, review, and insight generation.
Utilizes modeling, simulation, and machine‑learning–driven predictions to support decision‑making for candidate selection, formulation optimization, and risk assessment.
Leverages literature, public datasets, and internal data—or proposes new experiments—to validate computational models and test model‑generated hypotheses.
Partners closely with teams across R&D, Medical Safety, and Regulatory Affairs to integrate computational findings with experimental evidence and guide project strategy.
Communicates complex computational approaches and data-derived insights to technical and non‑technical audiences through clear reports, presentations, and scientific deliverables.
Contributes to manuscripts, conference materials, and external collaborations.
Maintains broad and current knowledge of toxicology, computational biology, and data science trends, actively interpreting and applying emerging research to R&D initiatives.