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 role is ideal for a 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.

We are especially interested in candidates who are deeply fluent in the use of large language models, agentic AI systems, modern software engineering practices, and scalable approaches for harmonizing and modeling large, complex datasets.

The successful candidate will contribute to multiple government‑funded and institutional research initiatives, including a recently launched project focused on using large‑scale human data to better understand biological aging, resilience, healthspan, and age‑related disease risk. This role will help develop innovative AI‑enabled systems for organizing, harmonizing, analyzing, modeling, and interpreting large datasets generated across multiple collaborators, institutions, platforms, and data types.

We are looking for someone who is not only technically strong, but also inventive, entrepreneurial, and capable of rapidly building solutions. The ideal candidate will be comfortable working at the intersection of AI, software engineering, data science, and biomedical research, and will bring the creativity needed to design new approaches for managing and modeling complex scientific data.

• Develop AI‑enabled systems for large‑scale data harmonization and modeling: design, build, and implement computational systems that support the organization, harmonization, modeling, and interpretation of large biomedical datasets.
• Build pipelines to extract, standardize, and validate metadata and data dictionaries.
• Create systems to support multi‑modal data integration across omics, clinical, demographic, imaging, and functional datasets.
• Develop scalable approaches for identifying patterns, inconsistencies, and missing information across large datasets.
• Support model development for prediction, classification, clustering, and biological interpretation.
• Prototype AI tools that improve research productivity, reproducibility, and scientific discovery.
• Build workflows using large language models, retrieval‑augmented generation, vector databases, tool‑calling agents, and automated reasoning systems.
• Design AI agents capable of interacting with structured and unstructured scientific data.
• Develop systems that assist with literature mining, data annotation, hypothesis generation, and biological interpretation.
• Evaluate the performance, limitations, and reliability of AI‑enabled tools in biomedical research contexts.
• Support responsible, reproducible, and well‑documented use of AI in federally funded research.
• Collaborate 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.

• Translate scientific goals into computational tools and workflows.
• Participate in project meetings and present technical progress.
• Create clear documentation, diagrams, and technical specifications.
• Support manuscript preparation, grant writing, figure generation, and reporting.
• Work with diverse teams to improve data transfer, management, and analysis systems.
• Help establish best practices for AI‑assisted data science in…