AI Data Engineer — Pharmacokinetics, Dynamics, and Metabolism (senior Associate)

Pfizer Pfizer · Pharma · CT

This role focuses on data engineering and MLOps within the pharmaceutical domain, specifically for ADME science. The primary responsibilities involve automating scientific workflows, preparing AI-ready datasets, and building scalable data pipelines to support AI/ML initiatives. The role requires strong Python skills, understanding of data architecture for AI/ML, and cloud deployment experience.

What you'd actually do

  1. Support data processing, visualization, and exploratory analysis for computational workflows using Python.
  2. Enable machine learning and data science workflows by preparing, structuring, and validating data for AI‑enabled ADME use cases.
  3. Design, curate, and maintain well‑structured datasets and databases for chemical, biological, and toxicology data, aligned with Pfizer data standards and quality expectations.
  4. Contribute to data architecture efforts by implementing scalable, reusable data pipelines and AI‑ready data assets.
  5. Apply best practices for data integrity, security, and regulatory compliance, including version control, testing, documentation, and reproducible code.

Skills

Required

  • Python
  • pandas
  • NumPy
  • scikit-learn
  • Git
  • data architecture principles
  • cloud environment (AWS, GCP, or Azure)

Nice to have

  • heterogeneous datasets
  • Dash
  • Streamlit
  • LLM concepts
  • RAG concepts
  • basic chemistry concepts
  • chemical structures
  • cross domain teams
  • databases
  • moving prototypes from development to production

What the JD emphasized

  • AI-enabled ADME use cases
  • AI-ready datasets
  • AI/ML workflows
  • AI/ML workflows
  • regulatory compliance

Other signals

  • automating scientific workflows
  • curating and engineering AI-ready datasets
  • enabling scalable, reusable AI solutions