Product Manager, Data Center Optimization (Global Service Operations)

Vertiv is a global leader in mission-critical infrastructure technologies, and they are seeking a Product Manager for Data Center Optimization. The role involves leading the operationalization of high-performance Power and Cooling models, managing optimization software lifecycles, and ensuring infrastructure performance through advanced mathematical validation methods.

Responsibilities:

• Oversee the deployment and lifecycle of AI Optimization software, managing the architectural nuances between Cloud-based predictive modeling and On-Premises real-time execution layers
• Apply advanced statistical and mathematical methods to validate the efficacy of predictive cooling algorithms, ensuring that the software’s 'Workload-Aware' logic aligns with actual thermodynamic and electrical outcomes
• Develop and maintain global service standards for integrated Next Optimization power and cooling models, ensuring that electrical and thermal management systems operate as a single, workload-aware entity
• Define requirements for the global rollout of Next AI Optimization, ensuring service technicians and remote engineers have the tools to maintain peak hardware performance
• Work with GSO to embed Next AI Optimization logic into standard operating procedures (SOPs), allowing global teams to preemptively address thermal challenges through workload-aware cooling strategies
• Establish the global framework for auditing Recovered Capacity and verifying performance gains through rigorous data analysis to trigger performance-based contract milestones
• Shift Global Service SLAs from 'Response Time' to 'Performance Guarantees,' leveraging real-time telemetry to benchmark facility health against optimal conditions

Requirements:

• 5+ years in Product Management or Technical Operations within Global Service organizations, specifically in DCIM, BMS, or Critical Infrastructure
• Strong background in mathematics (Linear Algebra, Calculus, Statistics, or Numerical Analysis) to interpret AI model outputs and perform complex ROI and performance calculations
• Deep understanding of AI Optimization software stacks, including the integration of Cloud-based training/inference engines with On-Premises control logic for low-latency mechanical response
• Strong understanding of liquid cooling (CDUs, Secondary Fluid Networks) and integrated electrical power distribution models
• Ability to map complex telemetry across power and cooling domains to physical service workflows and hardware safety guardrails
• Experience in Performance-Based Contracting and ROI modeling, demonstrating value through service-led optimization
• Proven ability to scale software-enabled services across multiple regions with varying technical maturity levels