Data Science Fellow - AI/NLP

Responsibilities

• Design and implement AI/NLP methods for biomedical literature mining and structured protocol knowledge extraction.
• Develop benchmark datasets, annotation guidelines, and evaluation pipelines for scientific information extraction.
• Build and evaluate RAG, in-context learning, fine-tuning, graph matching, entity normalization, and KG query workflows.
• Analyze extraction errors, model behavior, retrieval failures, grounding quality, and biological ambiguity.
• Collaborate with software engineers to integrate research methods into usable tools and reproducible pipelines.
• Collaborate with organoid biologists and domain experts to translate biological protocol knowledge into computable representations.
• Prepare manuscripts, conference abstracts, technical reports, design documents, and open-source research artifacts.
• Help define research milestones, evaluation criteria, and publication strategy for protocol intelligence work.

Requirements

• PhD in computer science, computational biology, bioinformatics, biomedical informatics, NLP, machine learning, data science, or a related field.
• Strong Python programming skills.
• Demonstrated research experience with NLP, information extraction, LLMs, RAG, transformers, structured prediction, or scientific text mining.
• Ability to design controlled computational experiments, create benchmark datasets, and analyze results rigorously.
• Familiarity with biological, biomedical, or scientific data.
• Strong written communication skills and interest in publishing methods-oriented research.
• Comfort working with complex, evolving research codebases and interdisciplinary teams.

Preferred Qualifications

• Experience with scientific document processing, PDF parsing, biomedical literature mining, or methods-section extraction.
• Experience with knowledge graphs, ontologies, graph databases, graph algorithms, or semantic data modeling.
• Hands-on experience with fine-tuning LLMs, LoRA/QLoRA, Hugging Face, PyTorch, or API-based model evaluation.
• Hands-on experience with prompt engineering, structured JSON extraction, schema validation, tool use, or agentic LLM workflows.
• Hands-on experience with RAG systems, vector search, graph-augmented retrieval, or natural-language query over structured data.
• Exposure to bioinformatics concepts (e.g., sequence alignment, clustering, or phylogenetic analysis).
• Background in stem cell biology, organoids, developmental biology, wet-lab protocols, or biological assays.