Senior Scientist I/II, Computational Biology & AI

AbbVie•North Chicago, IL

1d•$109,500 - $208,500

About The Position

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.

Requirements

Strong scientific foundation in biology, toxicology, pharmacology, or a related discipline with demonstrated ability to critically evaluate experimental data and biological mechanisms.
Ability to understand scientific objectives, identify key data and knowledge gaps, and translate problems into effective computational strategies.
Working proficiency in Python and/or R with the ability to develop reproducible analytical workflows and scientific software solutions.
Experience applying statistical, machine learning, and data analysis methods to biological, translational, or safety-related datasets.
Demonstrated ability to independently scope projects, prioritize competing needs, and execute complex initiatives across multiple functions.
Strong understanding of the strengths, limitations, and appropriate application of computational approaches, including classical statistics, machine learning, and AI.
Proven ability to communicate effectively with scientists from diverse disciplines and levels of computational expertise.
Experience leading or influencing cross-functional collaborations to deliver scientific outcomes
Senior Scientist I: Bachelor’s Degree or equivalent education and typically 10 years of experience, Master’s Degree or equivalent education and typically 8 years of experience, PhD and no experience necessary. PhD in Biology, Toxicology, Pharmacology, Computational Biology, Biochemistry, or a related life sciences discipline ideal.
Senior Scientist II: Bachelor’s Degree or equivalent education and typically 12 years of experience, Master’s Degree or equivalent education and typically 10 years of experience, PhD and typically 4 years of experience. PhD in Biology, Toxicology, Pharmacology, Computational Biology, Biochemistry, or a related life sciences discipline ideal.

Nice To Haves

Experience working with toxicology, pathology, safety pharmacology, or clinical safety datasets.
Hands-on laboratory research experience providing direct understanding of experimental design, assay variability, and biological data generation.
Experience integrating multimodal datasets spanning molecular, cellular, tissue, animal, and clinical domains.
Familiarity with cloud computing, scalable data processing, and large biological data platforms.
Experience with modern AI methodologies, including large language models and generative AI applications in scientific research.
Experience developing visualization tools, dashboards, or user-facing applications for scientific audiences.

Responsibilities

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.
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.
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.
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.