Principal Ml/ai Cheminformatics Scientist

Pfizer Pfizer · Pharma · CT

Principal ML/AI Cheminformatics Scientist at Pfizer applying data-driven approaches to drug discovery, developing and deploying machine learning models for molecular property prediction and ADME properties. Collaborates with cross-functional teams to integrate AI into discovery workflows.

What you'd actually do

  1. Drive scientific and technical advancement in PDM’s AI and cheminformatics capabilities by developing and delivering robust, validated workflows and algorithms that directly support drug discovery.
  2. Design, build, and deploy innovative cheminformatics and AI workflows for small molecule discovery, with clear deliverables including prioritized compound lists, well-defined chemical libraries, and actionable structure-activity relationship analyses.
  3. Collaborate closely with medicinal chemists, biologists, and data scientists to integrate computational approaches into experimental and analytical workflows.
  4. Continuously monitor and evaluate emerging advances in AI, cheminformatics, and computational drug discovery, proactively leading the adoption of promising new methodologies.
  5. Develop novel algorithms and analytical strategies tailored to Pfizer’s proprietary datasets and key scientific questions, with measurable outcomes such as validated models, software tools, and contributions to scientific literature or patent filings.

Skills

Required

  • PhD in Cheminformatics, Computational Chemistry, Bioinformatics, Data Science, or a closely related field and a minimum of 4 years of relevant drug discovery experience in a pharmaceutical or biotech environment OR Master’s degree in Cheminformatics, Computational Chemistry, Bioinformatics, Data Science, or a closely related field and a minimum of 9 years of relevant drug discovery experience in a pharmaceutical or biotech environment
  • Experience applying AI methods to medicinal chemistry or cheminformatics challenges, including work with molecular representations, chemical fingerprints, generative models, transformer architectures, pretraining strategies, property prediction, virtual screening, and ADMET modeling.
  • Strong proficiency in Python, with hands-on experience using cheminformatics libraries and tools such as RDKit, OpenEye, DeepChem, InChI, and SMILES/SMARTS.
  • Proven track record of creatively applying computational techniques to address problems relevant to pharmaceutical research and development.
  • Experience communicating effectively and collaborating with diverse teams
  • Experience delivering results in a fast-paced environment while managing multiple priorities.

Nice to have

  • Relocation support available
  • Relocation assistance may be available based on business needs and/or eligibility.

What the JD emphasized

  • minimum of 4 years of relevant drug discovery experience in a pharmaceutical or biotech environment
  • minimum of 9 years of relevant drug discovery experience in a pharmaceutical or biotech environment
  • Experience applying AI methods to medicinal chemistry or cheminformatics challenges
  • Strong proficiency in Python
  • Proven track record of creatively applying computational techniques to address problems relevant to pharmaceutical research and development.

Other signals

  • applying AI methods to medicinal chemistry or cheminformatics challenges
  • developing and deploying machine learning models for molecular property prediction
  • leveraging proprietary chemical and biological datasets to inform decision-making