Postdoctoral Fellow, Computational Genetic and Safety Data Science

Postdoctoral Fellow, Computational Genetic and Safety Data Science

Company Description
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 several key therapeutic areas – immunology, oncology, neuroscience, eye care – and products and services in our Allergan Aesthetics portfolio.

Program Overview
Abb Vie needs outstanding individuals willing to challenge themselves to find the best solutions for our patients. The Abb Vie Postdoctoral Program is one way we are doing just that. Postdoctoral Fellows serve as technical experts who investigate, develop, and optimize new methods and techniques to address critical project or functional area needs. Participants will improve existing or develop new laboratory methods and processes, read and adapt literature to accomplish assignments, and should have mastery of a range of experimental techniques and data analysis specific to their area of expertise.

Role Overview
In this cross-functional role, the post‑doctoral fellow will develop AI‑driven methodologies to bridge the gap between genomic evidence and safety outcomes, addressing a critical challenge in pharmaceutical development. This position sits at the intersection of artificial intelligence, human genetics, and safety assessment, supporting Abb Vie’s commitment to leveraging genetic insights to improve clinical success rates. Working under the mentorship of experts in genetics, patient safety, and AI/ML, the postdoc will have access to Abb Vie’s unparalleled genetic and safety datasets.

This project represents a key initiative within Abb Vie’s broader AI strategy, with direct applications to accelerate drug development and reduce safety‑related attrition across multiple therapeutic areas.

• Identify, curate, and process internal and external genetic and safety‑related datasets, applying sophisticated data‑science methodologies.
• Design and implement agentic AI systems capable of autonomous data querying, extraction, and interpretation across traditionally siloed safety and genomic domains.
• Develop advanced data harmonization techniques and standardized ontologies to enable integration of genetic, preclinical, and clinical safety datasets.
• Implement graph‑based retrieval‑augmented generation (RAG) methods to enhance knowledge extraction and information synthesis.
• Develop cross‑pathway analytical methods using AI to predict safety outcomes for multiple targets and combination therapies.
• Collaborate with research teams and data scientists to design data‑driven strategies using machine learning/AI methods that support discovery and preclinical safety studies.
• Generate and validate experimental hypotheses derived from AI models in collaboration with in‑vitro teams.
• Publish research findings in peer‑reviewed journals and present at scientific conferences.

Basic Qualifications

• PhD in Computational Biology, Bioinformatics, Computer Science, Human Genetics, Toxicology, or related field (summer and fall graduates are also welcome to apply).
• Strong programming skills in Python with experience in data manipulation, analysis, and machine learning libraries.
• Demonstrated experience in applying advanced AI/ML methods to biological problems.
• Experience with database querying, management systems, and data extraction techniques for large datasets.
• Knowledge of natural language processing (NLP) and/or large language models (LLMs).
• Experience with genomic data analysis, including variant interpretation or population genetics.
• Proficiency in statistical analysis and interpretation of complex biological datasets.
• Demonstrated ability to develop data visualization tools and interfaces for biological data representation.
• Excellent communication skills, with ability to translate complex computational findings to diverse stakeholders.
• Track record of scientific creativity and problem‑solving in research activities.

Preferred Qualifications

• Experience with agentic AI systems, prompt engineering, or multi‑agent frameworks.
• Familiarity with…