Postdoctoral Fellow-R&D Science Engineer

Role Summary

Postdoctoral Fellow – R&D Science Engineer focused on applying AI/ML to drug discovery and development. Embedded within Pfizer's core research organizations, including Quantitative Systems Pharmacology, Inflammation & Immunology, Oncology, Vaccines, and Internal Medicine. You will translate complex biology and clinical challenges into deployable AI solutions, informing molecular design, study strategy, and decision-making across the drug development lifecycle. This fellowship is designed for early-career scientists within one to two years of postgraduate training who want their work to matter beyond publications.

Responsibilities

• Design, develop, and apply advanced AI and machine learning methods to address high impact scientific challenges across the drug discovery and development lifecycle.
• Translate models across datasets, modalities, and disease areas using approaches such as transfer learning, representation learning, and domain adaptation.
• Perform integrative and meta-analyses of large-scale, multisource datasets to generate robust, generalizable insights.
• Curate, harmonize, and quality control complex scientific and clinical datasets to enable rigorous statistical analysis and machine learning model development.
• Collaborate closely with multidisciplinary teams spanning biology, chemistry, pharmacology, clinical science, and data science to ensure AI solutions are scientifically grounded and decision relevant.
• Communicate scientific findings clearly and transparently through internal forums and external publications, producing high impact, peer reviewed work while safeguarding confidential information and supporting reproducibility.

Qualifications

• Required: PhD in a relevant discipline such as Computer Science, Machine Learning, Artificial Intelligence, Biomedical Engineering, Applied Mathematics, Statistics or Biostatistics, Computational Biology, Systems Biology, Computational Chemistry, Bioinformatics, Biomedical Informatics, Immunology, or a related field.
• Required: Early career researcher (no more than 2-years of postdoc working experience).
• Required: Able to commit to a minimum two-year postdoctoral fellowship.
• Required: Demonstrated scientific achievement through first author publications, peer reviewed contributions, or significant scientific presentations.
• Required: Experience applying AI/ML to real world problems, including predictive modeling, generative models, representation learning, or ML system design.
• Required: Proficiency in Python and modern ML frameworks such as PyTorch and/or TensorFlow.
• Required: Experience working with large, complex, or heterogeneous datasets, including data curation, model evaluation, and scalable computing environments (cloud and/or HPC).
• Required: Ability to collaborate across disciplines including biology, chemistry, pharmacology, statistics, engineering, or clinical science, translating computational approaches into scientific context.
• Required: Familiarity with reproducible and responsible AI practices, including version control, transparent reporting, and awareness of model limitations and bias.
• Required: Strong communication skills, intellectual curiosity, and a mission driven interest in advancing science and improving patient outcomes.
• Preferred: Exposure to applied research or R&D workflows where analytical outputs inform decisions.
• Preferred: Code or reproducible research artifacts (e.g., GitHub, GitLab).