VP, Head of FinOps

Jefferies Financial Group•New York, NY

67d

About The Position

The Enterprise AI FinOps 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.

Requirements

Significant experience in FinOps, cloud financial management, technology finance, cost management, infrastructure economics, or a related analytical role within a complex organization.
Demonstrated experience building cost transparency, dashboards, forecasts, allocation models, variance analysis, and optimization recommendations for consumption-based technology services.
Strong understanding of cloud and technology cost drivers, pricing constructs, usage telemetry, budgeting, forecasting, and vendor economics.
Ability to analyze complex datasets and translate findings into actionable decisions, trade-offs, and executive-ready narratives.
Experience partnering across Engineering, Infrastructure, Finance, Procurement, Product, and business stakeholders.
High attention to detail, strong control mindset, commercial judgment, and excellent written and verbal communication skills.

Nice To Haves

Experience managing or analyzing AI, machine learning, GPU, model API, or token-based consumption costs.
Familiarity with FinOps Foundation principles, cloud cost-management practices, and showback or chargeback methodologies.
Experience within investment banking, capital markets, regulated financial services, or major cloud, AI, observability, or business-intelligence tooling environments.

Responsibilities

Define and implement the FinOps 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, resilience, and business value.
Identify and track optimization opportunities, including lower-cost routing, reduced token or compute consumption, caching and reuse, service consolidation, and commercial renegotiation.
Develop bottom-up forecasts for AI consumption and program costs using adoption, user, workload, model, token, compute, and pricing assumptions.
Establish baselines and scenario analyses for growth, new use cases, model changes, pricing changes, vendor commitments, and infrastructure choices.
Partner with program and business owners to translate demand plans into financial forecasts and identify potential funding or capacity constraints.
Perform critical analysis of financial performance, including whether the program has over- or underspent, whether spend produced the intended value, and what better alternatives are available.
Support annual planning, periodic forecasting, accruals, variance explanations, and financial inputs to business cases and funding gates.
Maintain clear documentation of assumptions, data limitations, allocation rules, and changes to forecasts or methodologies.
Provide business and technology stakeholders with clear visibility into available model, platform, and delivery options rather than presenting a single prescribed solution.
Help use-case owners understand the cost consequences of design, usage, adoption, and scaling decisions before commitments are made.
Partner with Business Transformation Leads, Product Managers, and pod teams to incorporate consumption cost and unit economics into business cases and ROI tracking.
Challenge assumptions constructively and recommend alternatives that improve value without compromising required performance, controls, or user outcomes.
Develop practical guidance, playbooks, and review checkpoints that enable teams to make financially informed AI decisions.
Promote shared accountability for AI value across Finance, Engineering, Product, Procurement, and the business.
Partner with Procurement, Legal, Infrastructure, and Technology leadership to evaluate pricing models, enterprise agreements, discounts, credits, commitments, and renewal options.
Analyze provider and model economics to support build, buy, partner, and hosting decisions.
Monitor commitment utilization and recommend actions to reduce waste, improve purchasing leverage, and avoid unfavorable lock-in.
Create transparent comparisons of vendor proposals and commercial structures for decision-makers.
Support negotiations with data and scenarios that reflect expected adoption, consumption volatility, and business demand.
Establish data-quality and reconciliation controls so AI financial reporting is complete, consistent, traceable, and decision-ready.
Coordinate with Finance, Controllers, Internal Audit, Risk, and Compliance on evidence, control requirements, and audit readiness related to AI spend and allocation.
Maintain documented methodologies for cost attribution, allocation, optimization measurement, and reporting.
Identify gaps in billing, tagging, application ownership, usage telemetry, or organizational data and drive remediation with accountable teams.
Ensure sensitive financial and consumption data is handled in accordance with applicable policies and access requirements.