AI Data Scientist-Furman lab

Position Summary

The Buck Institute for Research on Aging is seeking an exceptional, highly motivated AI Data Scientist / Agentic AI Engineer to join a collaborative research team focused on aging, computational biology, multi-omics, and translational data science.

This position is ideal for a creative, technically outstanding individual with a Master’s degree or equivalent experience who has demonstrated excellence through high-impact projects, awards, hackathons, publications, startup experience, open-source contributions, or other evidence of exceptional technical ability. Candidates should be deeply fluent in large language models, agentic AI systems, modern software engineering practices, and scalable approaches for harmonizing and modeling large, complex datasets.

The candidate will help design, build, and implement computational systems that support the organization, harmonization, modeling, and interpretation of large biomedical datasets.

Responsibilities include:

• Developing agentic AI workflows to support data curation, quality control, documentation, and analysis
• Designing LLM‑powered tools to help harmonize large datasets across cohorts, studies, institutions, and assay platforms
• Building pipelines to extract, standardize, and validate metadata and data dictionaries
• Creating systems to support multi‑modal data integration across omics, clinical, demographic, imaging, and functional datasets
• Developing scalable approaches for identifying patterns, inconsistencies, and missing information across large datasets
• Supporting model development for prediction, classification, clustering, and biological interpretation
• Prototyping AI tools that improve research productivity, reproducibility, and scientific discovery

• Building workflows using large language models, retrieval‑augmented generation, vector databases, tool‑calling agents, and automated reasoning systems
• Designing AI agents capable of interacting with structured and unstructured scientific data
• Developing systems that assist with literature mining, data annotation, hypothesis generation, and biological interpretation
• Evaluating the performance, limitations, and reliability of AI‑enabled tools in biomedical research contexts
• Supporting responsible, reproducible, and well‑documented use of AI in federally funded research
• Collaborating with bioinformaticians and domain experts to translate research needs into functional computational tools

• Transcriptomics, including single‑cell and bulk RNA‑seq
• Proteomics
• Metabolomics
• Epigenetics and biological aging clocks
• Clinical and phenotypic datasets
• Survey data
• Integrative multi‑omics
• Dimensionality reduction and clustering
• Classification methods and predictive modeling
• Drug repurposing
• Network analysis and pathway enrichment
• Computer vision and feature extraction, as applicable

• Translating scientific goals into computational tools and workflows
• Participating in project meetings and presenting technical progress
• Creating clear documentation, diagrams, and technical specifications
• Supporting manuscript preparation, grant writing, figure generation, and reporting
• Working with diverse teams to improve data transfer, management, and analysis systems
• Helping establish best practices for AI‑assisted data science in biomedical research

• Master’s degree in Computer Science, Data Science, Computational Biology, Bioinformatics, Applied Mathematics, Statistics, Engineering, or a related field; equivalent professional, entrepreneurial, or technical experience will also be considered
• Demonstrated experience building AI, data science, machine learning, or software engineering systems
• Strong proficiency in Python
• Experience using large language models, AI APIs, or LLM‑based developer tools
• Experience with modern software engineering practices, version control, testing, documentation, and collaborative…