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 biology and computation. We are seeking a scientist with a strong biological foundation who has developed sufficient computational expertise to independently translate scientific problems into fit-for-purpose analytical solutions.

The successful candidate will understand how biological data are generated, appreciate the experimental and mechanistic context underlying safety findings, and possess the technical judgment to select, design, and implement computational approaches that address real scientific needs. 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 actionable insights and scientific tools.

Success in this role requires scientific leadership, cross-functional influence, and the ability to drive projects from problem definition through execution and implementation.

Key Responsibilities

Scientific Problem Solving & Strategy

• Partner with research scientists and safety experts to define critical scientific questions and identify opportunities where computational approaches can accelerate decision-making.
• Translate complex biological and toxicological challenges into analytical strategies that are scientifically grounded, practical, and scalable.
• Evaluate alternative computational approaches and select methods that best align with the biological context, available data, and business objectives.
• Serve as a trusted scientific advisor, providing guidance on study design, data interpretation, and appropriate use of machine learning and AI technologies.

Cross-Functional Scientific Leadership

• Act as a scientific translator between bench scientists, toxicologists, pathologists, clinicians, and computational teams.
• Build strong partnerships across Development Sciences to understand workflows, pain points, and decision-making processes.
• Lead multidisciplinary initiatives from concept through implementation, ensuring solutions are scientifically relevant and broadly adoptable.
• Drive alignment among stakeholders with diverse technical backgrounds and priorities.

Computational Solution Development

• Design, develop, and deploy predictive models, analytical workflows, and decision-support tools that address preclinical and translational safety challenges.
• Integrate and analyze diverse data sources, including toxicology, pathology, pharmacology, genomics, chemistry, molecular profiling, and clinical datasets.
• Develop reproducible computational pipelines and user-friendly applications that enable scientists without programming expertise to leverage advanced analytical methods.
• Collaborate with computational and data engineering teams to ensure solutions are scalable, maintainable, and fit for long-term use.

Communication & Scientific Influence

• Clearly communicate methods, findings, limitations, and recommendations to both technical and non-technical audiences.
• Present scientific insights in a way that facilitates decision-making and advances program strategy.
• Foster adoption of computational approaches by demonstrating scientific value and practical impact.