Data Science / AI Intern – Literature Mining & Graph Modeling
About The Position
AstraZeneca is seeking Master’s and PhD students studying Biology, Computer Science, Chemistry, Physics, Engineering, Biomedical Science, Pharmacology, Data Science, Bioinformatics, or a related discipline for a 10-week internship role at our site in Waltham, MA from June 01, 2026- August 07, 2026. This internship sits at the intersection of data engineering, biomedical NLP, and translational science, enabling faster insight generation for R&D teams. Position Description: Build an end-to-end pipeline turning literature (papers, abstracts, patents) into a standardized knowledge graph with contextualized evidence. Handle source selection, inclusion/exclusion criteria, updates, and data snapshots. Develop NLP for entity recognition, relation extraction, assertion detection, and context tagging (drug, indication, resistance, biomarker, outcome). Encode domain relations (e.g., Drug–mechanism→Gene/Pathway; Biomarker–modulates→Outcome; ADC–targets→Antigen). Map entities to controlled vocabularies; manage synonyms, disambiguation, and canonical IDs. Implement edge-level confidence scoring (source quality, claim type, co-occurrence, citations, model certainty) with full evidence provenance. Build graph storage (property graph or RDF) and queryable APIs. Deliver interactive visualization (UI or notebook) with filters, context toggles, and evidence drill-down. Define metrics, run error analyses, and validate with scientific stakeholders. Ensure reproducibility and documentation: version models/data; record architecture, assumptions, benchmarks; provide user guides. Present outcomes to data science, oncology, and translational medicine teams.
Requirements
- Master’s and PhD students studying Biology, Computer Science, Chemistry, Physics, Engineering, Biomedical Science, Pharmacology, Data Science, Bioinformatics, or a related discipline.
- Candidates must have an expected graduation date after August 2026.
- US Work Authorization is required at time of application.
- This role will not be providing OPT support.
- NLP and ML: NER, relation extraction, transformers; Python-based workflows.
- Graph/data modeling: experience with Neo4j, NetworkX, or RDF/SPARQL.
- Reproducibility: version control, environment management, documentation.
- Soft skills: problem-solving, communication, collaboration.
- Ability to report onsite to Waltham, MA site 3-5 days per week.
- This role will not provide relocation assistance.
Nice To Haves
- Domain knowledge: genes, pathways, biomarkers, therapeutic modalities (incl. ADCs) preferred.
Responsibilities
- Build an end-to-end pipeline turning literature (papers, abstracts, patents) into a standardized knowledge graph with contextualized evidence.
- Handle source selection, inclusion/exclusion criteria, updates, and data snapshots.
- Develop NLP for entity recognition, relation extraction, assertion detection, and context tagging (drug, indication, resistance, biomarker, outcome).
- Encode domain relations (e.g., Drug–mechanism→Gene/Pathway; Biomarker–modulates→Outcome; ADC–targets→Antigen).
- Map entities to controlled vocabularies; manage synonyms, disambiguation, and canonical IDs.
- Implement edge-level confidence scoring (source quality, claim type, co-occurrence, citations, model certainty) with full evidence provenance.
- Build graph storage (property graph or RDF) and queryable APIs.
- Deliver interactive visualization (UI or notebook) with filters, context toggles, and evidence drill-down.
- Define metrics, run error analyses, and validate with scientific stakeholders.
- Ensure reproducibility and documentation: version models/data; record architecture, assumptions, benchmarks; provide user guides.
- Present outcomes to data science, oncology, and translational medicine teams.
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What This Job Offers
Career Level
Intern
Number of Employees
5,001-10,000 employees
