Scientist IISenior Scientist I, Computational Toxicology

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 

In Vitro Toxicology & Experimental Science 

• 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. 

Scientific Problem Solving & Strategy 

• 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. 

Computational Solution Development 

• 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 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. 

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 experimental concept through data generation modeling and implementation. 

• Drive alignment among stakeholders with diverse technical and experimental backgrounds. 

Communication & Scientific Influence 

• Clearly communicate experimental methods computational approaches findings limitations and recommendations to both technical and non-technical audiences. 

• Present integrated experimental and computational insights in a way that facilitates decision-making and advances program strategy. 

• Foster adoption of both new in vitro methodologies and computational approaches by demonstrating scientific value and practical impact.

Qualifications :

Senior Scientist I 

• Bachelors Degree or equivalent education and typically 10 years of experience Masters Degree or equivalent education and typically 8 years of experience PhD and no experience necessary. PhD in Toxicology Pharmacology Cell Biology Biochemistry Computational Biology or a related life sciences discipline ideal. 

Senior Scientist II 

• Bachelors Degree or equivalent education and typically 12 years of experience Masters Degree or equivalent education and typically 10 years of experience PhD and typically 4 years of experience. PhD in Toxicology Pharmacology Cell Biology Biochemistry Computational Biology or a related life sciences discipline ideal. 

Required Experience and Skills 

• Hands-on laboratory experience designing executing and troubleshooting in vitro toxicology or cell-based assays (e.g. cell culture high-content imaging ELISA/immunoassays flow cytometry or similar techniques). 

• Strong scientific foundation in toxicology pharmacology cell biology 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 experimental and 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 including data generated from the candidates own experiments. 

• Demonstrated ability to independently scope projects prioritize competing needs and execute complex initiatives spanning both wet-lab and computational domains. 

• 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 including both experimentalists and computational specialists. 

• Experience leading or influencing cross-functional collaborations to deliver scientific outcomes.

Preferred Qualifications 

• Experience with New Approach Methodologies (NAMs) including 3D models organoids organ-on-chip or high-throughput/high-content in vitro screening platforms. 

• Experience working with toxicology pathology safety pharmacology or clinical safety datasets. 

• 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. 

Additional Information :

Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: 

• The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location and we may ultimately pay more or less than the posted range. This range may be modified in the future. 
• We offer a comprehensive package of benefits including paid time off (vacation holidays sick) medical/dental/vision insurance and 401(k) to eligible employees.
• This job is eligible to participate in our short-term incentive programs. 

Note: No amount of pay is considered to be wages or compensation until such amount is earned vested and determinable. The amount and availability of  any bonus commission incentive benefits or any other form of compensation and benefits that are allocable to a particular employee remains in the Companys sole and absolute discretion unless and until paid and may be modified at the Companys sole and absolute discretion consistent with applicable law. 

AbbVie is an equal opportunity employer and is committed to operating with integrity driving innovation transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled. 

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Work :

No

Employment Type :

Full-time