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Salary
≈ $64k – $144k per year (Estimated)
Location
In office (Westerville)

Confirmed on the employer's own hiring board on Nov 5, 2026. First seen by Alion on Aug 10, 2026. Vertiv scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Vertiv is an American company that supplies the power and cooling infrastructure data centres depend on, formed in 2016 when Emerson Electric sold its network power division to Platinum Equity. Its products cover uninterruptible power supplies, switchgear and busway, precision air and liquid cooling, racks and the monitoring software that runs them, sold to hyperscale operators, colocation providers and enterprises. Headquartered in Westerville, Ohio and listed on the New York Stock Exchange, it has become one of the most direct beneficiaries of artificial intelligence build-out because dense computing requires liquid cooling it already makes.

Position Summary

The AI Insights & Governance Analyst will drive visibility and measurable value realization across Vertiv's AI portfolio. This role owns the analytics layer for enterprise AI-tracking use case value, tool adoption, usage and proficiency, and governance compliance-and translates it into executive-ready insight that informs investment and prioritization decisions. The ideal candidate is fluent in the AI landscape and AI lifecycle, strong in analytics and stakeholder communication, but need not be a deep technical/engineering practitioner.

Key Responsibilities

  • Track and quantify business value delivered by AI use cases-cost savings, productivity gains, revenue impact-across Vertiv's global processes (SIOP, NPDI, OTO, OTC) and functions.
  • Build and maintain centralized dashboards covering: AI use case portfolio, enterprise AI tool adoption, and user engagement/usage trends/proficiency levels.
  • Monitor and report KPIs for the Enterprise AI Tool, including adoption, usage frequency, response quality, and business outcomes.
  • Analyze enterprise usage of internal and external AI tools and surface optimization opportunities.
  • Define and track AI governance metrics: compliance, approved-model usage, and adherence to enterprise AI policies.
  • Partner with business, IT, and AI teams to standardize value-measurement frameworks and reporting structures.
  • Produce executive-level scorecards, summaries, and insights for the ELT/leadership audience.
  • Support intake and prioritization of new AI use cases with analytics on expected value and impact.
  • Identify gaps in adoption, training, or tooling and recommend data-driven improvements.

Requirements

  • Bachelor’s degree in business Analytics, Information Systems, Data Analytics, or a related field.
  • 2-4 years in business analytics, reporting, or data analysis roles, including hands-on work within AI/GenAI initiatives.
  • Solid working knowledge of the AI/GenAI landscape, tools, and vendors-able to speak credibly with both business and technical stakeholders without requiring a deep engineering background.
  • Strong analytical skills with proven experience in data visualization and reporting tools (Power BI, QuickSight, or equivalent).
  • Ability to translate business metrics into structured reporting and insight.
  • Experience working across cross-functional teams (business, IT, product).
  • Strong communication skills for executive-facing reporting.

Preferred Qualifications

  • Direct experience supporting AI/GenAI programs or enterprise AI initiatives.
  • Familiarity with the AI lifecycle (use case intake, deployment, governance, monitoring).
  • Knowledge of enterprise AI platforms and ecosystems (e.g., AWS AI services, internal AI platforms).
  • Experience defining KPIs and performance-measurement frameworks.
  • Exposure to data platforms (e.g., Snowflake or similar).

Core Skills

  • Business analytics and KPI development
  • Data visualization and dashboarding
  • AI adoption, usage, and proficiency analysis
  • Stakeholder communication and executive reporting
  • Governance tracking and compliance awareness

Performance Measures

  • Accuracy and completeness of AI value tracking across use cases
  • Adoption, engagement, and proficiency metrics across Enterprise AI Tools
  • Quality and usability of dashboards and executive reports
  • Improvement in visibility of AI portfolio performance
  • Contribution to governance maturity and compliance reporting

#LI-RB1

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