Data Science & AI Librarian, Stanford Law School Job at Inside Higher Ed in Stan

Data Science & AI Librarian, Stanford Law School

Empirical insight and responsible data practices strengthen legal scholarship and public impact. The Data Science & AI Librarian empowers faculty and students with consultation and training in Python, NLP/LLM workflows, data acquisition and curation, and reproducible research. Partnering with campus data resources, this role advances rigorous, bias‑aware analysis and preserves high‑value datasets and code for long‑term discovery and reuse.

Job Purpose

The incumbent serves as the library’s lead technical expert on data assessment and analysis, focusing on how data quality, structure, and bias function within AI models to ensure responsible and effective implementation of cutting‑edge technologies. This position reports to the Associate Director for Access Services.

Core Duties

• Formulate and implement library‑wide policies or best practices for data science and AI services.
• Serve as the library’s principal expert on emerging AI technologies, advising on adoption and leading pilot projects.
• Establish reproducible research practices (Git, environments/notebooks) and deposit datasets/code in campus repositories with rich documentation; assign persistent identifiers and apply metadata standards per repository policy.
• Advise on research ethics/IRB and sensitive‑data handling for AI/ML projects; coordinate with IRB where applicable.
• Support the Reference & Instruction team by serving as the escalation path for complex data/AI queries, co‑running office hours, creating internal playbooks and reusable notebooks, and training staff on RAG/verification to improve first‑contact resolution.
• Coordinate with and mentor other library staff in data and AI competencies to integrate services across research support teams.
• Support LLM/NLP workflows (e.g., RAG pipelines, evaluation/guardrails) for legal text analytics; produce reusable evaluation notebooks (hallucination checks, citation validation, bias probes).
• Design and teach technical skills workshops (Python/NLP, RAG, visualization, reproducibility).
• Lead on data ethics, bias, and evaluation policy for AI in research contexts; publish guidance and checklists.
• Assist with data acquisition (APIs, compliant web scraping, FOIA); cleaning/transforming; and analysis; advise on Data Management Plans for grants.
• Liaise with campus data science institutes, HPC, and central library data services; use HPC or lightweight cloud runtimes when scale is needed.
• Coordinate with the E‑Resources Librarian to ensure text/data mining and API use comply with database licenses and robots/terms of use.
• Teach or co‑teach short courses or embedded modules on empirical legal methods, text analytics, and visualization.
• Participate in the shared AI & Innovation intake queue; triage and co‑staff multi‑facet projects.
• Serve on the Library AI Advisory Group to align research practices with tool governance and classroom guidance.
• May supervise Data Scientists, Data Curators, and/or Data Assistants.

Success Metrics

Number of faculty projects supported; number of datasets/code deposited; number of reproducible runs verified.

Technology Scope

In addition to AI‑specific platforms, this position will work with related (“AI‑adjacent”) technologies and software including learning management tools, legal research platforms, web publishing, accessibility testing, analytics dashboards, identity and access (Single Sign‑On), APIs, lightweight integrations, programming or notebook environments (Python/Jupyter), version control (Git), and service/ticket systems.

Minimum Requirements

• Master’s degree in Library/Information Science, Computer Science, Data Science, Statistics, Information Science, or an equivalent combination of education and experience (e.g., 3+ years applied data science supporting academic or legal research).
• Portfolio required (GitHub/notebooks or comparable) demonstrating Python/NLP, data management, and reproducible research practices.
• Familiarity with legal information sources and research workflows strongly preferred; JD not required.

Knowledge, Skills & Abilities

• Required
  • Demonstrated experience with Python and its libraries relevant to data analysis or machine learning (e.g., Pandas, Scikit‑learn).
  • Strong understanding of AI concepts, including machine learning, and experience with data assessment methodologies.
  • Documented experience in providing technical training or consultations.
• Preferred
  • Familiarity with legal information sources and research processes.
  • Experience orchestrating LLM workflows (e.g., RAG pipelines, evaluation/guardrails) and working with tools like Jupyter; familiarity with Git for collaboration.
  • Knowledge of data management plans and reproducible research practices.
  • Experience with data visualization tools (e.g., Tableau).
  • Hands‑on experience with NLP/LLM tooling such as spaCy and Hugging Face; ability to build RAG notebooks and run small evaluations.
  • Orchestration frameworks familiarity such as LangChain or LlamaIndex for rapid prototypes.
  • Proficiency with tools like Jupyter, GitHub, and environment management for reproducible workflows.
  • Proficiency with SQL and API data acquisition patterns; light web scraping with ethics/compliance awareness.
  • Comfortable packaging a project so it runs the same on any machine (Docker) and, when a laptop isn’t enough, using campus high‑performance computing (HPC) or a small cloud server to process larger datasets or speed up jobs.

Benefits & Professional Development

• Health & Wellness: Multiple medical, dental, and vision plans; health savings and flexible spending accounts; and access to wellness facilities and financial incentives.
• Retirement: A generous 403(b) retirement plan with university contributions and matching.
• Time Away: Substantial paid time off, including vacation, 11 paid holidays, a two‑week winter closure, sick leave, and baby bonding leave.
• Professional Growth: An annual professional development stipend and a tuition reimbursement program for degree programs.
• Work/Life Balance: Programs to support a healthy work/life balance, including child care subsidy grants, back‑up dependent care, and free commute passes.

Schedule & Compensation

Full‑time, fixed‑term (two‑year limited term). Pay range: $140,758 – $177,489 per annum. Additional benefits outlined above.

Seniority Level

Mid‑Senior level

Employment Type

Full‑time

Job Function

Engineering and Information Technology