Senior Research Scientist I/II, Computational Biology & Toxicology
AbbVie
June 26, 2026
Remote friendly (North Chicago, IL)
United States
$109,500 - $208,500 USD yearly
Clinical Research and Development
Responsibilities:
- Serve as a scientific translator between wet-lab researchers and computational infrastructure, ensuring fit-for-purpose solutions.
- Work with scientists to understand workflows, identify bottlenecks, and co-design practical, reproducible, scalable computational solutions.
- Develop user-friendly tools, pipelines, and applications for scientists without a computational background.
- Partner with research scientists, data scientists, and safety experts to design, implement, and validate ML/AI strategies for discovery and preclinical safety questions.
- Curate, harmonize, and integrate multi-modal datasets (chemical, genomic, molecular, in vitro, pathology, clinical) into scalable workflows for safety insight and risk prediction.
- Translate computational findings into predictive models and user-friendly applications to support drug discovery and development decisions.
- Communicate methods/results to multidisciplinary stakeholders (technical and non-technical).
Qualifications:
- Senior Scientist I: BS + ~10 yrs OR MS + ~8 yrs OR PhD + no experience.
- Senior Scientist II: BS + ~12 yrs OR MS + ~10 yrs OR PhD + 4 yrs.
- PhD in Computational Biology, Biology, Pharmacology, Biochemistry, or related field (postdoc/industry preferred).
- Strong biology foundation; ability to evaluate experimental data and contextualize computational outputs mechanistically.
- Coding fluency in Python (preferred) or R; clean, functional, reproducible scientific code.
- Working knowledge of ML for biological/safety datasets; strong stats for hypothesis testing, Bayesian inference, regression, multivariate, time-series.
- Advanced ML knowledge (deep learning; clustering; classification such as SVMs, random forests, gradient boosting).
Preferred:
- Experience with pathology and/or safety datasets; histopathology/clinical pathology integration.
- Hands-on wet lab experience.
- Scalable computing (parallel/cloud) and database querying.
- Generative AI/LLMs in scientific context.
- Data visualization/interface development for non-technical users.
Benefits (explicitly stated): Paid time off; medical/dental/vision insurance; 401(k) for eligible employees; eligible for long-term incentive programs.
- Serve as a scientific translator between wet-lab researchers and computational infrastructure, ensuring fit-for-purpose solutions.
- Work with scientists to understand workflows, identify bottlenecks, and co-design practical, reproducible, scalable computational solutions.
- Develop user-friendly tools, pipelines, and applications for scientists without a computational background.
- Partner with research scientists, data scientists, and safety experts to design, implement, and validate ML/AI strategies for discovery and preclinical safety questions.
- Curate, harmonize, and integrate multi-modal datasets (chemical, genomic, molecular, in vitro, pathology, clinical) into scalable workflows for safety insight and risk prediction.
- Translate computational findings into predictive models and user-friendly applications to support drug discovery and development decisions.
- Communicate methods/results to multidisciplinary stakeholders (technical and non-technical).
Qualifications:
- Senior Scientist I: BS + ~10 yrs OR MS + ~8 yrs OR PhD + no experience.
- Senior Scientist II: BS + ~12 yrs OR MS + ~10 yrs OR PhD + 4 yrs.
- PhD in Computational Biology, Biology, Pharmacology, Biochemistry, or related field (postdoc/industry preferred).
- Strong biology foundation; ability to evaluate experimental data and contextualize computational outputs mechanistically.
- Coding fluency in Python (preferred) or R; clean, functional, reproducible scientific code.
- Working knowledge of ML for biological/safety datasets; strong stats for hypothesis testing, Bayesian inference, regression, multivariate, time-series.
- Advanced ML knowledge (deep learning; clustering; classification such as SVMs, random forests, gradient boosting).
Preferred:
- Experience with pathology and/or safety datasets; histopathology/clinical pathology integration.
- Hands-on wet lab experience.
- Scalable computing (parallel/cloud) and database querying.
- Generative AI/LLMs in scientific context.
- Data visualization/interface development for non-technical users.
Benefits (explicitly stated): Paid time off; medical/dental/vision insurance; 401(k) for eligible employees; eligible for long-term incentive programs.