Serve as a scientific translator between wet-lab researchers and computational infrastructure - understanding experimental design, data provenance, and biological context well enough to ensure fit-for-purpose solutions
Engage directly with scientists to understand existing laboratory and analytical workflows, identify bottlenecks, and co-design computational solutions that are practical, reproducible, and scalable.
Develop user-friendly tools, pipelines, and applications designed for scientists without a computational background, enabling broader Development Sciences teams to leverage computational insights
Partner with research scientists, data scientists, and safety experts to design, implement, and validate machine learning/AI strategies that address key discovery and preclinical safety questions.
Curate, harmonize, and integrate multi-modal datasets including chemical, genomic, molecular, in vitro, pathology, and clinical sources, into scalable workflows that support safety insight generation and risk prediction
Translate computational findings into predictive models, analytical tools, and user-friendly applications that support decision-making in drug discovery and development.
Clearly communicate methods and results to multidisciplinary stakeholders, tailoring messages for both technical and non-technical audiences
Senior Scientist I Qualifications: Bachelor's Degree and typically 10 years of experience OR Master's Degree and typically 8 years of experience, OR PhD and no experience necessary.
Senior Scientist II Qualifications: Bachelor's Degree and typically 12 years of experience OR Master's Degree and typically 10 years of experience, OR PhD and 4 years of experience
PhD in Computational Biology, Biology, Pharmacology, Biochemistry, or a related life science field, with meaningful exposure to computational methods through coursework, dissertation research, or applied experience. Postdoctoral or industry experience preferred
A genuine scientific foundation in biology - whether through formal training, research experience, or applied industry work - sufficient to critically evaluate experimental data, identify biological confounders, and contextualize computational outputs in mechanistic terms.
Scientific coding fluency in Python (preferred) or R. We do not expect a software engineering background - we expect the ability to write clean, functional, reproducible code in service of scientific questions.
Working knowledge of machine learning applied to biological or safety datasets, with the ability to select and justify methods based on scientific context, not just algorithmic performance.
Strong foundation in statistical and applied analytical methods, including hypothesis testing, Bayesian inference, regression, multivariate, and time-series analyses.
Expertise in advanced machine learning, including deep learning, supervised/unsupervised clustering, and classification algorithms (e.g., SVMs, random forests, gradient boosting).
Demonstrated ability to communicate computational approaches and results to non-computational scientists, including presenting analytical strategies and translating findings into actionable scientific insights.
Preferred
Demonstrated experience working with pathology and/or safety datasets; familiarity with integrating histopathology, clinical pathology, or safety study data into computational workflows.
Hands-on wet lab experience (e.g., experimental design, assay development, or mechanistic biology studies) that informs a deeper understanding of data generation, variability, and biological constraints.
Experience with scalable computing (parallelization, cloud platforms) and database querying for large biological datasets.
Experience with generative AI (GANs, VAEs) or large language models (LLMs) in a scientific context.
Experience in data visualization and interface devel
Additional Information
The Computational Toxicology group is dedicated to advancing in-silico approaches that improve the prediction and mechanistic understanding of drug safety across small molecules, biologics, and emerging modalities. This role sits at the intersection of biological science and computational innovation - and that intersection is intentional.
We are looking for a scientist with deep domain knowledge in biology who has also developed computational skills to independently design, build, and deploy data-driven solutions. The ideal candidate can stand at the bench conceptually, understand what drives experimental variability, and architect computational solutions that reflect biological reality.
The role focuses on integrating diverse data sources - including pharmacology, toxicology, genomics, pathology, chemistry, and clinical datasets - into predictive and interpretable models. You will work directly with research scientists to understand their workflows, co-design solutions, and build tools that make computational capabilities accessible to generalist scientists across Development Sciences.
Abbvie · North Chicago, IL