Senior Research Scientist I​/II, Computational Biology & Toxicology

About Abb Vie

Abb Vie'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 key therapeutic areas such as immunology, oncology, and neuroscience, as well as products and services in the Allergan Aesthetics portfolio. For more information, visit

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.

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.

• Serve as a scientific translator between wet‑lab researchers and computational infrastructure, ensuring fit‑for‑purpose solutions by understanding experimental design, data provenance, and biological context.
• 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:
  Bachelors Degree with ~10 years of experience, Masters Degree with ~8 years, or PhD with no experience required.
• Senior Scientist II:
  Bachelors Degree with ~12 years of experience, Masters Degree with ~10 years, or PhD with 4 years.
• PhD in Computational Biology, Biology, Pharmacology, Biochemistry, or a related life science field, with substantial exposure to computational methods through coursework, dissertation research, or applied experience. Postdoctoral or industry experience preferred.
• Strong scientific foundation in biology with the ability to critically evaluate experimental data and contextualize computational outputs in mechanistic terms.
• Scientific coding fluency in Python (preferred) or R, with an emphasis on clean, functional, reproducible code.
• Working knowledge of machine learning applied to biological or safety datasets, selecting and justifying methods based on scientific context.
• 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…