AI Enablement & Governance Analyst

At EVERSANA, we are proud to be certified as a Great Place to Work across the globe. We’re fueled by our vision to create a healthier world. How? Our global team of more than 7,000 employees is committed to creating and delivering next-generation commercialization services to the life sciences industry. We are grounded in our cultural beliefs and serve more than 650 clients ranging from innovative biotech start-ups to established pharmaceutical companies. Our products, services and solutions help bring innovative therapies to market and support the patients who depend on them. Our jobs, skills and talents are unique, but together we make an impact every day. Join us!

Across our growing organization, we embrace diversity in backgrounds and experiences. Improving patient lives around the world is a priority, and we need people from all backgrounds and swaths of life to help build the future of the healthcare and the life sciences industry. We believe our people make all the difference in cultivating an inclusive culture that embraces our cultural beliefs. We are deliberate and self-reflective about the kind of team and culture we are building. We look for team members that are not only strong in their own aptitudes but also who care deeply about EVERSANA, our people, clients and most importantly, the patients we serve. We are EVERSANA.

The AI Adoption & Governance Analyst operationalizes AI standards across the Patient Services engineering organization and proves the SDLC transformation in metrics. This role is the connective tissue of the AI Hub COE: rolling out AI development tooling to Dev, QA, and BA teams; owning the governance artifacts that keep AI use safe and compliant; and reporting the adoption and ROI story to leadership.

It is the right role for someone who blends analytical rigor with strong communication and change-management instincts — someone who can drive real adoption of new ways of working, not just publish a policy.

Mission: Operationalize AI standards across BA, QA, and Dev — and prove the SDLC transformation in measurable adoption and ROI.

Tools & Stack

- AI (evaluation & support): Claude, Gemini Enterprise, Einstein.
- Dev tools supported: Claude Code, GitHub Copilot, Cursor.
- Dashboards & data: Looker / Tableau, BigQuery.
- Documentation: Confluence, Notion, JIRA.

First-Year Success Measures

- AI development tooling adopted across Patient Services engineering, with measurable AI velocity gains tracked and reported.
- Governance framework operational: approved-tools list, prompt-library process, risk register, and compliance-review workflow all live.
- Monthly adoption reporting and quarterly executive ROI dashboards established and delivered.

Responsibilities

Roll out AI development tooling — Claude Code, GitHub Copilot, Cursor — as engineering standards across the Patient Services teams.; Train and onboard Dev, QA, and BA staff into AI-augmented ways of working, run office hours, certification, and internal forums.; Own the AI best-practice wiki, training materials, and the agent registry.; Own and maintain the prompt library governance process, including quality gates before templates are promoted.; Conduct HIPAA/PHI compliance reviews of AI tool usage, coordinate with InfoSec on data-flow approval and risk sign-off.; Maintain the approved-tools list and the AI risk register.; Support model-selection and BAA-verification processes led by the AI Engineering & Enablement Lead.; Define and track AI adoption KPIs: AI velocity, code-generation percentage, defect-rate delta, time-to-merge, and story points per sprint.; Author monthly team adoption reports and quarterly executive ROI dashboards for the CTO and CFO.; Surface adoption gaps and recommend interventions to the AI Engineering & Enablement Lead.

Requirements

3+ years in a technical analyst, program enablement, developer-experience, or similar role.; Strong data analysis and dashboarding skills.; Excellent technical writing and documentation ability.; Demonstrated change-management and training-delivery experience.; Working fluency with AI tooling ecosystems (Claude, Gemini, AI coding assistants) — able to teach and support their use.; Understanding of HIPAA, SOC 2, or comparable privacy/compliance fundamentals.; Strong cross-team coordination and stakeholder-communication skills.

Nice to have

Background in healthcare or life-sciences technology.; Experience supporting or governing an AI or developer-tooling rollout at scale.; Familiarity with JIRA, Confluence, and BI tools (Looker, Tableau, or BigQuery).