VP, Head of FinOps

Job Description

The Enterprise AI Fin Ops Lead will establish financial transparency, cost governance, and optimization across Jefferies’ Enterprise AI Program. The role will create a trusted view of AI spend across model and token consumption, cloud and compute, infrastructure, platforms, licenses, partnerships, and usage-based services.

The Enterprise AI Fin Ops Lead will establish financial transparency, cost governance, and optimization across Jefferies’ Enterprise AI Program. The role will create a trusted view of AI spend across model and token consumption, cloud and compute, infrastructure, platforms, licenses, partnerships, and usage-based services.

This individual will define financial guardrails, identify cost and usage anomalies, support forecasting and allocation, and provide clear recommendations that balance cost, performance, risk, and business value.

Working closely with Enterprise AI Program Management, Technology Finance, Engineering, Infrastructure, Procurement, Controllers, and divisional stakeholders, this individual will own AI cost analytics and dashboarding, forecasting, allocation and chargeback support, optimization recommendations, and executive reporting. The role requires strong financial judgment, technical curiosity, analytical rigor, and the ability to translate complex consumption data into practical decisions and measurable value.

• Define and implement the Fin Ops operating model for the Enterprise AI Program, including scope, governance, decision rights, controls, service levels, and reporting cadences.
• Establish a common taxonomy and ownership model for AI costs across models, tokens, cloud, compute, infrastructure, platforms, vendors, users, applications, divisions, and cost centers.
• Partner with Technology Finance and the AI PMO to embed cost discipline into use-case design, architecture, vendor selection, budgeting, forecasting, and value-realization processes.

• Own the authoritative view of AI spend, commitments, consumption, forecasts, allocations, and realized optimization opportunities.
• Design dashboards that provide visibility by model, provider, application, use case, user, team, division, environment, and cost category where data is available.
• Produce recurring executive-ready analysis of actuals versus budget and forecast, including run-rate changes, anomalies, concentration risks, emerging cost drivers, and required actions.

• Define guardrails for variable AI consumption, including budgets, thresholds, alerts, quotas, exception processes, and escalation protocols.
• Monitor usage and cost patterns to identify uncontrolled growth, idle or duplicative services, inefficient configurations, policy breaches, and avoidable spend.
• Partner with platform, engineering, infrastructure, and security teams to implement proportionate controls and track corrective actions to completion.
• Establish a disciplined process for investigating anomalies and ensuring corrective actions are assigned, tracked, and completed.
• Support transparent showback, chargeback, or allocation approaches so consuming teams understand and take ownership of the costs they generate.
• Ensure cost controls are proportionate and do not unnecessarily restrict legitimate experimentation or high-value business outcomes.

• Maintain a current commercial and cost view of approved models, providers, platforms, and service options available to the Enterprise AI Program.
• Partner with technical teams to evaluate model, context, compute, hosting, and service-tier choices against cost, performance, quality, control,…