Pre Clinical Safety Data Scientist

Description

Kenvue is currently recruiting for a:

Pre Clinical Safety Data Scientist

What we do

At Kenvue , we realize the extraordinary power of everyday care.

Built on over a century of heritage and rooted in science, we're the house of iconic brands - including NEUTROGENA®, AVEENO®, TYLENOL®, LISTERINE®, JOHNSON'S® and BAND-AID® that you already know and love.

Science is our passion; care is our talent.

Who We Are

Our global team is ~ 22,000 brilliant people with a workplace culture where every voice matters, and every contribution is appreciated.

We are passionate about insights, innovation and committed to delivering the best products to our customers.

With expertise and empathy, being a Kenvuer means having the power to impact millions of people every day.

We put people first, care fiercely, earn trust with science and solve with courage - and have brilliant opportunities waiting for you! Join us in shaping our future-and yours.

For more information , click here .

Role reports to:
Manager NA-Toxicology

Location:
North America, United States, New Jersey, Summit

Work Location:
Hybrid

What you will do

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.



* Omics & Computational Method Development: Designs and implements innovative omics‑based and computational toxicology approaches to address key challenges in product development.


* AI & Data Science Integration: Incorporates AI tools, large language models (LLMs), and agentic workflows into daily scientific operations to accelerate discovery, documentation, review, and insight generation.


* Modeling, Simulation & Predictive Analytics: Utilizes modeling, simulation, and machine‑learning-driven predictions to support decision‑making for candidate selection, formulation optimization, and risk assessment.


* Hypothesis Generation & Validation: Leverages literature, public datasets, and internal data-or proposes new experiments-to validate computational models and test model‑generated hypotheses.


* Cross‑Functional Scientific Collaboration: Partners closely with teams across R&D, Medical Safety, and Regulatory Affairs to integrate computational findings with experimental evidence and guide project strategy.


* Scientific Communication & Reporting: Communicates complex computational approaches and data-derived ins...