Senior Computational Scientist I - Toxicology

Company Description

AbbVie'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 – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.

Job Description

The Computational Toxicology group is dedicated to advancing in-silico methods that enhance the prediction and understanding of safety and toxicology for both small and large molecules. Team members will work with diverse biology-related datasets, ranging from pharmacology, toxicology, genomics, and chemistry, applying data science and machine learning techniques. The primary goal is to leverage data effectively and identify useful insights. This role focuses on leveraging computational expertise to process and analyze biological datasets for predictive modeling and novel safety-related discoveries.

Responsibilities:

• Collaborate with research teams and data scientists to design and implement data-driven strategies, utilizing machine learning/AI methods to support discovery and preclinical safety studies. Work closely with scientists to co-develop tools and solutions tailored to the most relevant and pressing research problems, ensuring that computational approaches align with scientific objectives.
• Design, develop, and implement solutions with applications including, but not limited to, chemistry, in vitro, preclinical, clinical, and genomic datasets.
• Identify, curate, and process internal and external biology and safety-related datasets. Apply data science methodologies to harmonize and analyze complex datasets, uncovering associations that inform safety assessments.
• Develop predictive models, analytical tools, and intuitive user interfaces to translate computational findings into actionable insights, enabling safety risk prediction.
• Communicate results and methods clearly to both scientific and non-technical audiences, ensuring effective knowledge transfer across t