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About This Role
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. 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.Key Responsibilities
1\. Develop AI\-enabled systems for large\-scale data harmonization and modeling
The candidate will help design, build, and implement computational systems that support the organization, harmonization, modeling, and interpretation of large biomedical datasets. Responsibilities may 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
2\. Apply LLMs, agentic AI, and modern machine learning approaches to biomedical research
Responsibilities may include:* 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
3\. Support large\-scale data science and computational biology projects
The candidate may contribute to analyses involving:* 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
4\. Collaborate across interdisciplinary teams
The candidate will work closely with computational biologists, data scientists, principal investigators, research staff, software engineers, and external collaborators. Responsibilities may include:* 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
Qualifications
Required Education and Experience* 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
Required Skills* 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
Preferred Qualifications* 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
Compensation and Benefits* Salary range: $60,000–$75,000, commensurate with experience
- Full\-time position
- Exciting, collaborative work environment at the forefront of aging research, AI, and computational biology
- Opportunity to help build AI\-enabled systems for large\-scale biomedical discovery
- Generous benefits package, including:
+ Health insurance
+ Paid parental leave
+ Generous paid time off
+ 401(k) with 5% employer match
- Work visa sponsorship may be available for qualified candidates
About the Buck Institute
Our success will ultimately change healthcare. At the Buck Institute for Research on Aging, we aim to end the threat of age\-related diseases for this and future generations by bringing together the most capable and passionate scientists from a broad range of disciplines to identify and impede the ways in which we age.
The Buck is an independent, nonprofit institution located in Marin County, California, with the goal of increasing human healthspan, or the healthy years of life. Globally recognized as a pioneer and leader in efforts to target aging — the number one risk factor for diseases including Alzheimer’s disease, Parkinson’s disease, cancer, macular degeneration, heart disease, and diabetes — the Buck seeks to help people live better longer.
We are an equal opportunity employer and strive to create an atmosphere where diversity of identity, experience, and background are welcomed, valued, and supported. Candidates who contribute to this diversity are strongly encouraged to apply.To Apply
Interested candidates should click the Apply button to complete the online application.
Please upload:* Resume or CV
- A brief statement describing your technical interests, relevant AI/data science experience, and examples of systems, tools, or projects you have built
- Names and contact information for three references, if available
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Salary Context
This $60K-$75K range is in the lower quartile for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Buck Institute, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($67K) sits 66% below the category median. Disclosed range: $60K to $75K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Buck Institute AI Hiring
Buck Institute has 1 open AI role right now. They're hiring across Data Scientist. Based in Novato, CA, US. Compensation range: $75K - $75K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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