AI Enablement & Governance Analyst

Jobgether; United States; Remoto; USD 90.000 - 130.000; Tempo pieno

Vantaggi offerti da questo lavoro

Healthcare benefits

AI tooling access

Cross-functional collaboration

Professional development

Diversity and inclusion

Descrizione del lavoro

Jobgether seeks an AI Enablement & Governance Analyst to bridge AI strategy with practical deployment. You will coordinate tool rollouts (Claude Code, GitHub Copilot, Cursor), train teams, and maintain governance and risk frameworks.

You will drive adoption, measure impact with dashboards, and produce reports for executives, while refining best practices and templates across departments.

Competenze

• At least 3 years in a technical analyst, enablement, developer experience, or related role.
• Strong data analysis and dashboarding capabilities with the ability to translate metrics into insights.
• Excellent technical writing and documentation skills.
• Experience with change management, employee training, and technology enablement.
• Working knowledge of AI tooling ecosystems (Claude, Gemini, AI coding assistants) and ability to teach their use.
• Understanding of privacy/compliance requirements (HIPAA, PHI, SOC 2).
• Strong cross-functional coordination and stakeholder communication skills.
• Ability to manage multiple initiatives and establish clear processes for adoption across technical teams.

Mansioni

• Drive AI adoption and enablement across Development, QA, and Business Analysis teams.
• Deliver training, office hours, and certification initiatives for AI workflows.
• Maintain the AI best-practice wiki, training resources, and agent registry.
• Manage prompt library governance, quality gates, and template reviews.
• Support compliance and risk management with data-flow approvals and risk sign-offs.
• Maintain governance records including approved-tools lists and AI risk registers.
• Track AI adoption and performance metrics (velocity, code-generation, defect-rate changes).
• Prepare monthly adoption reports and quarterly ROI dashboards for leadership.
• Identify improvement opportunities and communicate findings to the AI Engineering and Enablement Lead.

Conoscenze

Data analysis

Dashboarding

Technical writing

Change management

Stakeholder collaboration

Project enablement

Strumenti

Jira

Confluence

Looker

Tableau

BigQuery

Claude

Gemini Enterprise

Einstein

Claude Code

GitHub Copilot

Cursor

Notion

Descrizione del lavoro

Accountabilities

As an AI Enablement & Governance Analyst, you will connect AI strategy with practical implementation by supporting adoption, maintaining governance standards, and measuring the impact of AI across development teams. Your responsibilities will include:

• Drive AI adoption and enablement: Coordinate the rollout of AI development tools, including Claude Code, GitHub Copilot, and Cursor, across Development, QA, and Business Analysis teams.
• Deliver training and support: Train and onboard employees in AI-augmented workflows, organize office hours, manage certification initiatives, and facilitate internal knowledge-sharing forums.
• Maintain AI documentation: Own and update the AI best-practice wiki, training resources, and agent registry to ensure teams have access to consistent guidance.
• Manage AI governance: Maintain the prompt library governance process, establish quality gates, and review templates before they are approved for broader use.
• Support compliance and risk management: Conduct reviews of AI tool usage involving HIPAA and Protected Health Information (PHI), coordinate with Information Security on data-flow approvals, and support risk sign-offs.
• Maintain governance records: Keep the approved-tools list and AI risk register up to date, while supporting model-selection and Business Associate Agreement (BAA) verification processes.
• Track AI adoption and performance: Define and monitor key indicators such as AI velocity, code-generation percentage, defect-rate changes, time-to-merge, and story points per sprint.
• Prepare performance reports: Produce monthly adoption reports and quarterly executive ROI dashboards for senior technology and finance leadership.
• Identify improvement opportunities: Analyze adoption gaps, recommend targeted interventions, and communicate findings to the AI Engineering and Enablement Lead.

Requirements

The ideal candidate combines analytical expertise, technical communication skills, and practical experience driving technology adoption and organizational change. The following qualifications are required:

• At least 3 years of experience in a technical analyst, program enablement, developer experience, or related role.
• Strong data analysis and dashboarding capabilities, with the ability to translate metrics into actionable insights.
• Excellent technical writing and documentation skills.
• Demonstrated experience in change management, employee training, and technology enablement.
• Working knowledge of AI tooling ecosystems, including Claude, Gemini, and AI coding assistants, with the ability to teach and support their practical use.
• Understanding of HIPAA, PHI, SOC 2, or comparable privacy and compliance requirements.
• Strong cross-functional coordination and stakeholder communication skills.
• Ability to manage multiple initiatives, establish clear processes, and encourage adoption across technical teams.

The following qualifications would be considered an advantage:

• Experience in healthcare technology or the life sciences industry.
• Previous involvement in large-scale AI adoption, governance, or developer-tooling implementation.
• Familiarity with Jira and Confluence for project coordination and documentation.
• Experience with business intelligence and analytics tools such as Looker, Tableau, or BigQuery.
• Exposure to AI platforms and tools such as Claude, Gemini Enterprise, Einstein, Claude Code, GitHub Copilot, and Cursor.
• Familiarity with Notion and other knowledge-management platforms.

Benefits

• Opportunity to contribute to AI transformation initiatives within healthcare and life sciences technology.
• Exposure to modern AI development tools and enterprise AI governance practices.
• Cross-functional collaboration with engineering, QA, business analysis, information security, and executive teams.
• Opportunity to shape AI adoption standards, training programs, and performance measurement frameworks.
• Meaningful contribution to responsible AI practices supporting healthcare technology and patient-focused services.
• Inclusive work environment that values professional development, collaboration, innovation, and diversity.