Research AI & Data Enablement Specialist

University of California, San Francisco•San Francisco, CA

About The Position

Applies professional concepts and UCSF policies and procedures to increase successful researcher adoption of research data assets, computational environments, AI capabilities, and research technologies. Independently analyzes researcher needs and adoption barriers; identifies opportunities to apply AI and automation to streamline research processes, reduce manual effort, and improve researcher productivity; develops, tests, and operationalizes AI-enabled and automated workflow solutions within supported research environments; develops and maintains scalable onboarding, documentation, knowledge resources, workflow examples, and AI-assisted support capabilities. Validates researcher-facing workflows and recommends improvements to research services and user experience. Works across researchers, product management, data engineering, research infrastructure, and support teams to translate complex technical capabilities into practical guidance. Within defined procedures and practices, determines appropriate action and escalates product strategy, infrastructure priorities, architectural direction, service portfolio, funding priorities, and institutional communications decisions to responsible leadership.

Requirements

Bachelor's degree in a related area and/or equivalent experience/training.
Strong knowledge of and at least 5 years of experience in data science principles, practices, and tools such as Python, R, SQL, and others.
Broad knowledge of information technology concepts relevant to research data services, computational environments, AI-enabled capabilities, and user-facing research technologies.
Demonstrated experience applying generative AI-enabled tools, or AI-assisted automation to practical data, analytical, or research workflows, including identifying appropriate use cases, developing or configuring workflow solutions, testing outputs, and evaluating effectiveness.
Ability to analyze end-to-end research or data workflows and identify opportunities to use AI and automation to reduce manual effort, improve efficiency, increase reproducibility, or enable researcher self-service.
Demonstrated ability to translate complex technical concepts, system capabilities, and workflows into clear, practical guidance for technical and non-technical users.
Demonstrated experience authoring and maintaining technical documentation, onboarding materials, knowledge-base content, FAQs, workflow guides, or comparable user-enablement resources.
Working knowledge of research data workflows and the design and use of data sources, analytical environments, or computational tools sufficient to validate end-to-end user workflows and troubleshoot moderately complex issues.
Demonstrated working knowledge, skills and abilities associated with system problem identification and resolution.
Knowledge necessary to design, set up, operate and correct malfunctions involving technology systems.
Experience in incident response and digital forensics, including data collection, examination and analysis.
Demonstrated testing and workflow-validation skills, including reproducing user-reported issues, validating documentation accuracy, and supporting acceptance testing for user-facing features.
Familiarity with version-controlled documentation or code repositories and ability to create reusable technical examples, code snippets, sample notebooks, or reference workflows.
Thorough problem-solving and process-analysis skills; ability to identify recurring user needs and adoption barriers, analyze feedback and usage patterns, and recommend scalable improvements.
Familiarity with data quality, governance, privacy, security, and responsible data-use requirements in a research or comparable institutional environment.
Interpersonal and communication skills sufficient to work effectively with researchers, engineers, product managers, program leadership, and support personnel at varying levels of technical expertise.
Ability to work independently within defined procedures and practices, exercise sound judgment, manage multiple priorities, and meet deadlines while contributing effectively as part of a cross-functional team.

Nice To Haves

Experience developing AI-ready knowledge resources, AI-assisted support capabilities, agentic or automated workflows, retrieval-augmented applications, or comparable AI-enabled solutions.
Broad knowledge of databases and data systems.
Knowledge relating to complex database system design.
In-depth database querying and modeling skills working with moderately complex databases.
Knowledge of conceptual database design.
Skills necessary to develop procedures and standards relating to database management systems.
Skills associated with implementing database enhancements that improve performance and reliability of the system at the software, hardware and operating system level.
Experience supporting academic, biomedical, clinical, or other research communities and familiarity with researcher-centered service design or adoption practices.
Experience delivering onboarding sessions, office hours, technical presentations, or community-based user enablement.
Experience contributing to scoped research data, analytical, or consulting engagements and converting lessons learned into reusable organizational resources.

Responsibilities

Develop and maintain scalable onboarding pathways that enable researchers to use UCSF research data and AI infrastructure effectively.
Analyze common research workflows to identify opportunities for AI-enabled optimization and automation; develop, configure, test, and operationalize reusable AI-assisted workflows that reduce repetitive or manual research tasks and improve researcher productivity.
Create reusable technical examples, code snippets, workflow guides, and AI-assisted researcher support workflows; validate researcher workflows; improve discoverability of services and capabilities; identify opportunities to reduce user friction; and support adoption of new research capabilities.
Develop and maintain the institutional knowledge base supporting research data services.
Author researcher-facing and technical documentation; develop AI-ready knowledge resources; capture subject matter expertise across data assets, tools, and compute environments; standardize documentation; create reusable FAQs and troubleshooting resources; and maintain version-controlled documentation repositories so institutional knowledge is reusable by researchers and AI-assisted support systems.
Monitor and improve researcher success using research data ecosystem that includes data assets, tools, and research compute infrastructure.
Identify recurring support requests and adoption barriers; monitor documentation effectiveness; collect and analyze user feedback; measure user-success outcomes; and recommend improvements to onboarding and self-service experiences.
Evaluate researcher-facing capabilities and end-to-end research workflows to identify opportunities for AI-enabled optimization and automation.
Prototype and validate practical approaches for incorporating AI into supported research workflows, including opportunities to automate repetitive tasks, streamline data interaction and analysis processes, and improve researcher productivity.
Validate documentation accuracy, reproduce reported issues, develop illustrative code and data-analysis examples, sample notebooks, AI-assisted workflow patterns, and reference implementations; verify end-to-end workflows; and support acceptance testing.
Develop sufficient technical familiarity to evaluate and communicate AI-enabled workflows without serving as the primary application developer, data scientist, or research analyst.
Deliver onboarding sessions, targeted office hours and limited consultations; facilitate user communities; present new capabilities; gather researcher feedback; and escalate product or infrastructure improvement opportunities.
Use individual engagements primarily to identify systemic documentation, usability, or enablement improvements rather than provide long-term customized support.
Contribute to selected, limited-scope research data or analytical support engagements that advance researcher success, validate emerging capabilities, or inform scalable services.
Develop reusable workflows, code examples, and implementation patterns; identify recurring needs that may be addressed through documentation, platform enhancements, or new services; and communicate lessons learned to product management and program leadership.