AI Data Scientist-Furman lab

Buck Institute•Novato, CA

3d•$60,000 - $75,000

About The Position

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. 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, government-funded 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.

Requirements

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 development.
Ability to work independently, rapidly prototype solutions, and solve ambiguous technical problems.
Strong practical experience with large language models and AI-assisted workflows.
Interest or experience in agentic AI, tool-calling agents, retrieval-augmented generation, vector search, or automated workflow orchestration.
Strong analytical and problem-solving skills.
Ability to design systems for organizing, harmonizing, and modeling large datasets.
Comfort working with structured and unstructured data.
Excellent written and oral communication skills.
Strong attention to detail and commitment to reproducibility.
Ability to collaborate with both technical and non-technical team members.
High degree of creativity, initiative, and intellectual curiosity.

Nice To Haves

Evidence of exceptional technical achievement, such as hackathon wins, awards, competitive programming, startup experience, open-source contributions, publications, deployed products, or other high-impact projects.
Experience with biomedical, healthcare, clinical, or omics data.
Experience with APIs, cloud platforms, Docker, databases, or scalable data systems.
Experience with vector databases, embeddings, RAG systems, or AI agent frameworks.
Experience with Python-based data science libraries and machine learning frameworks.
Familiarity with data harmonization, metadata standards, ontologies, or research data repositories.
Experience working in fast-paced startup, academic, or highly collaborative environments.

Responsibilities

Develop AI-enabled systems for large-scale data harmonization and modeling, including designing, building, and implementing computational systems for organization, harmonization, modeling, and interpretation of large biomedical datasets.
Develop agentic AI workflows to support data curation, quality control, documentation, and analysis.
Design LLM-powered tools to help harmonize large datasets across cohorts, studies, institutions, and assay platforms.
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.
Apply LLMs, agentic AI, and modern machine learning approaches to biomedical research, including building 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.
Support large-scale data science and computational biology projects, including analyses involving transcriptomics, proteomics, metabolomics, epigenetics, clinical and phenotypic datasets, survey data, integrative multi-omics, dimensionality reduction, classification methods, drug repurposing, network analysis, and computer vision.
Collaborate across interdisciplinary teams, including computational biologists, data scientists, principal investigators, research staff, software engineers, and external collaborators.
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 biomedical research.