Senior Computational Toxicologist; Chemistry
Listed on 2026-01-03
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IT/Tech
Data Scientist, Machine Learning/ ML Engineer -
Research/Development
Data Scientist
We’re in relentless pursuit of breakthroughs that change patients’ lives. We innovate every day to make the world a healthier place.
To fully realize Pfizer’s purpose – Breakthroughs that change patients’ lives – we have established a clear set of expectations regarding “what” we need to achieve for patients and “how” we will go about achieving those goals.
Pfizer Research & Development serves as the beating heart of Pfizer's trailblazing product pipeline, the essence of our mission to bring life-changing medicines to the world.
Pfizer offers competitive compensation and benefits programs designed to meet the diverse needs of our colleagues.
The Senior Computational Toxicologist (Chemistry-focused) will lead and support informatics-driven initiatives to integrate chemical and toxicology datasets aimed at improving the overall nonclinical drug discovery and development decision-making and processes, including but not limited to the reduction of currently required toxicity studies, the analysis, uptake and incorporation of new approach methods (NAMs) and the integration of multi-modal data to improve portfolio and study-level decision-making.
This role combines advanced computational approaches with toxicology expertise to enable data-driven decision-making across drug discovery and development. The position will focus on building scalable data architectures and applying artificial intelligence/machine learning to transform drug safety assessment by advancing innovative approaches such as hybrid and virtual control study design implementation and multimodal predictive toxicology modeling.
Key Responsibilities
- Apply advanced R and/or Python programming for data processing, visualization, and statistical modeling.
- Implement machine learning workflows to predict toxicological outcomes and support AI-enabled safety assessments.
- Design, develop, curate, and maintain robust databases for chemical and toxicology data, ensuring data quality and compliance with Pfizer data standards.
- Collaborate with toxicologists, pathologists, data scientists, and informatics teams to integrate multi-modal datasets (chemical structures, in vitro/in vivo toxicology data) into next generation predictive toxicology models.
- Contribute to foundational data architecture strategies and implementation to enable AI-driven insights.
- Ensure data integrity, security, and adherence to regulatory requirements, while contributing to best practices for coding workflows, version control, and comprehensive documentation to support reproducibility and transparency.
- Communicate findings through reports, presentations, and publications to internal stakeholders and external scientific forums.
- Stay current with emerging computational toxicology methods and actively participate in team discussions to improve workflows and approaches.
Basic Qualifications
Education:
- Master's in Computational Chemistry/Toxicology, Chemistry, Toxicology, Data Science, or related field with 2+ years of experience
- Ph.D. in Computational Chemistry/Toxicology, Chemistry, Toxicology, Data Science, or related field with 0+ years of experience
Technical Skills
:
- Deep expertise in R and/or Python for data analysis and modeling following coding best practices, including version control, structured workflows, documentation, and reproducibility standards.
- Proven experience in database creation, management, and analysis
- Expertise in machine learning techniques and their application in chemical, biological and/or toxicological sciences.
- Understanding of data architecture principles to support AI workflows.
- Soft Skills:
Strong communication, collaboration, and problem-solving abilities.
Preferred Qualifications
- Experience with cloud-based data platforms and big data technologies.
- Knowledge of cheminformatics tools and toxicology ontologies and datasets (working with SEND formatted data).
- Track record of publications in computational toxicology or related fields.
- Domain Knowledge:
Working knowledge of toxicology data, including chemical safety assessments and regulatory frameworks
Candidate demonstrates a breadth of diverse leadership experiences and…
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