Senior Computational Toxicologist (Chemistry)

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 capabilities including: the ability to influence and collaborate with peers, develop and coach others, oversee and guide the work of other colleagues to achieve meaningful outcomes and create business impact.

PHYSICAL/MENTAL REQUIREMENTS

Ability to communicate clearly at internal and external meeting and across diverse teams. Office job involving computer use.

NON-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS

Limited travel for internal R&D site visits and to attend national and international scientific meeting, consortia and training opportunities.