Scientist II​/Senior Scientist , Computational Toxicology

Company Description

About Abb Vie

Abb Vie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about Abb Vie, please visit us at  Follow @abbvie on Linked In, Facebook, Instagram, X and You Tube.

Role Overview

The Computational Toxicology group is advancing the use of data science, machine learning, and AI to improve the prediction and mechanistic understanding of drug safety across small molecules, biologics, and emerging therapeutic modalities.

This role is intentionally positioned at the intersection of laboratory science and computation. We are seeking a hybrid scientist who is equally comfortable generating high-quality in vitro toxicology data at the bench and building the computational tools needed to interpret it. This individual will design and execute in vitro assays to generate mechanistic and predictive safety data, while also developing analytical pipelines, predictive models, and decision-support tools that extract maximum scientific value from that data — and from broader toxicology, pathology, and translational datasets.

The successful candidate will understand firsthand how in vitro biological data are generated — including assay design, cell culture systems, experimental variability, and mechanistic interpretation — and will apply that hands‑on knowledge to build computational approaches that are scientifically grounded and fit for purpose. This individual will serve as a scientific bridge across disciplines, partnering closely with toxicologists, pathologists, pharmacologists, clinicians, and data scientists to transform complex scientific questions into experimental data and actionable computational insights.

Success in this role requires dual fluency in laboratory science and computational methods, scientific leadership, cross-functional influence, and the ability to drive projects from experimental design through data analysis, modeling, and implementation.

Key Responsibilities

• Design, execute, and optimize in vitro toxicology assays (e.g., cell viability, high-content imaging, organ-on-chip, 3D/organoid, mitochondrial toxicity, genotoxicity, or immune cell-based assays) to support hazard identification and mechanistic investigation.

• Generate high-quality, reproducible experimental data to characterize compound-, biologic-, or modality-specific safety liabilities.

• Apply sound experimental design principles (controls, replicates, dose-response, assay validation) to ensure data are fit for downstream computational modeling.

• Troubleshoot assay performance, evaluate new in vitro model systems and technologies, and stay current with advances in alternative and New Approach Methodologies (NAMs).

• Collaborate with in vivo toxicologists and pathologists to contextualize in vitro findings against whole-animal and clinical safety signals.

• Partner with research scientists and safety experts to define critical scientific questions and identify where in vitro experimentation and/or computational approaches can accelerate decision-making.

• Translate complex biological and toxicological challenges into integrated experimental-and-analytical strategies that are scientifically grounded, practical, and scalable.

• Evaluate alternative in vitro models and computational methods, selecting approaches that best align with biological context, available data, and business objectives.

• Serve as a trusted scientific advisor on assay design, data interpretation, and appropriate use of machine learning and AI technologies.

• Design, develop, and deploy predictive models, analytical workflows, and decision-support tools that leverage in vitro-generated data alongside toxicology, pathology, pharmacology, genomics, chemistry, and clinical datasets.

• Build reproducible…