Pfizer Supply Chain Document Processing Externship
Build AI-powered document intelligence with Pfizer, automating healthcare and clinical supply documents using OCR, LLMs, and RAG.
Right now, reviewing non-GMP clinical supply documents at Pfizer often means opening PDFs in Adobe and hunting for information with Ctrl+F—and this project asks, what if AI did that work for us instead?
Externs will build a working AI-powered prototype that can read scanned and digital documents, extract key fields, classify files, and make everything searchable using modern AI tools. The focus is on real-world, messy documents and fast experimentation—not production polish—so we can see how much manual effort AI could realistically eliminate. By the end, Pfizer will have a concrete proof-of-concept that helps them evaluate where document automation creates real value and whether it’s worth scaling across the business.
Project Output
During this Externship, externs will:
- Ingest and parse scanned documents
- Extract and structure key data fields from multiple document types
- Implement intelligent search using RAG pipelines and metadata filters
- Build and test modular routing and classification logic
- (Optional) Deploy a lightweight user interface for search and querying
Technologies used:
- Python (scripting, data processing)
- PyMuPDF, pdfplumber (PDF parsing)
- OpenCV, PIL (image preprocessing)
- OCR Engines: Tesseract, PaddleOCR, EasyOCR
- Vector Search: LlamaIndex, FAISS, Chroma
- LLMs: Gemini API or open-source models (e.g., Mistral, Phi-2)
- UI Tools: Streamlit, Gradio (optional)
Final project outputs:
- Python pipeline to process document files
- Structured JSON output with field-level data and coordinates
- Functional RAG pipeline (Gemini or open-source)
- Searchable UI using Streamlit or Gradio
- Final report, system documentation, and demo
Why should you care? Here's what you'll get out of this Externship:
- Develop a portfolio-worthy AI/ML project
- Gain hands-on experience with cutting-edge tools and models
- Learn to build robust, production-level systems
- Improve data parsing, extraction, and visualization skills
- Tackle a real-world, high-impact business use case
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Pfizer is one of the world's largest publicly traded pharmaceutical companies, developing and manufacturing medicines and vaccines that reach hundreds of millions of people globally. They partnered with BioNTech to create the first FDA-authorized COVID-19 vaccine. In 2024, Pfizer reported $63 billion in revenue and products reaching 414 million patients worldwide—massive scale. Their clinical supply operations ensure that the right drugs get to the right patients at the right time—a process that depends on flawless documentation across shipping, logistics, and regulatory compliance.

Russell Vassallo
Russell Vassallo is a Senior Manager of Material Planning & Logistics at Pfizer, where he brings over a decade of experience supporting the company’s clinical supply pipeline. Based in Groton, Connecticut, Russell oversees materials management across Pfizer’s clinical portfolio—from early-phase API production through finished, packaged drug product shipped directly to patient studies. His work spans internal and external manufacturing partners, ensuring critical materials are available where and when they’re needed across the global clinical supply chain.
Russell began his career at Pfizer as an Environmental Health & Safety intern and has progressed through roles in inventory management and materials coordination, building deep operational expertise along the way. He holds a bachelor’s degree in Environmental Science with a concentration in Chemistry from the University of Connecticut and brings a strong interest in process improvement, automation, and sustainability. In addition to his professional work, Russell enjoys his time outside of work traveling, hiking, spending time on the water, and being active playing pickleball and hockey.
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Sign-up today for exposure to healthcare and supply chain while learning AI Automation!










