Lead the development and evaluation of advanced search and generative AI systems
Own complex problem areas end-to-end, drive methodological rigor in evaluation, and contribute to the technical direction of retrieval and RAG systems
Play a leading role in the design and optimization of lexical, vector, and hybrid retrieval systems at scale
Help architect and improve RAG pipelines, including retrieval strategies, prompt design, and system orchestration (e.g., LangGraph-based workflows)
Drive experimentation with embeddings, re-ranking models, and retrieval architectures to significantly improve relevance and user outcomes
Partner with engineering to ensure robust, scalable, and production-ready implementations
Help define and evolve evaluation strategies for search and generative AI systems across products
Contribute to responsible AI practices, including bias, fairness, and risk evaluation
Collaborate with domain experts, ontology engineers, and biomedical informaticians to integrate scientific taxonomies, citation networks, and clinical ontologies into retrieval systems
Take ownership of projects from problem definition through experimentation and deployment

Master’s or PhD in Computer Science, Data Science, Machine Learning, or a related field (or equivalent practical experience)
~3–5+ years of experience in data science, machine learning, or applied NLP
Strong hands-on experience with:
Search and retrieval systems (lexical, vector, hybrid)
RAG pipelines and LLM-based systems
Evaluation methodologies for ML / IR / GenAI
Advanced programming skills in Python
Experience with modern ML/NLP frameworks (e.g., PyTorch, Hugging Face, LangChain, LangGraph, Haystack)
Experience working with Databricks or similar distributed data/ML platforms
Strong understanding of experimentation design and statistical analysis

Senior Data Scientist

Key skills