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Lambda

Cloud-based GPU services for AI training

Senior Financial Planning and Analysis Analyst - Public Cloud

Corporate Finance & FP&AConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Have a Bachelor's degree in Finance, Accounting, Economics, or a related field.
  • Have 4+ years of experience in FP&A, corporate finance, GTM or sales finance, investment banking, or consulting, ideally with a focus on Cloud / AI infrastructure, SaaS, data centers, Telecom, or Hardware.
  • Have direct experience forecasting revenue in a consumption-based or usage-based business model.
  • Possess extensive experience in financial modeling and analysis, with a deep expertise in constructing complex financial models and interpreting financial statements to drive strategic decision-making.
  • Possess strong Excel skills and experience with financial software systems.
  • Be comfortable partnering directly with senior commercial and technical stakeholders.
  • Be able to work in an ambiguous environment with very little direction.

Responsibilities

  • Own the Public Cloud revenue forecast across on-demand, reserved, and committed-use offerings.
  • Build and maintain driver-based models linking capacity, utilization, and pricing to revenue.
  • Forecast new customer acquisition, expansion, contraction, and churn by product line.
  • Deliver monthly revenue forecasts along with variance analysis and commentary.
  • Partner with Accounting on billing, consumption reconciliation, and revenue recognition.
  • Own OpEx and headcount planning, tracking, and reporting for the Public Cloud organization.
  • Maintain hiring plans, compensation assumptions, and organizational change tracking against approved headcount.
  • Prepare monthly and quarterly budget vs. actual analysis, identify key drivers, and partner with business owners to explain performance.
  • Support accrual and reclassification inputs with Accounting through the monthly close.
  • Serve as the finance partner to Public Cloud GTM leadership on go-to-market planning and resource allocation.
  • Model sales capacity, quota coverage, and rep productivity; evaluate territory and quota scenarios.
  • Analyze pipeline conversion, bookings-to-revenue linkage, and funnel efficiency.
  • Build the business case for GTM investments and measure realized return.
  • Develop segment-level views of revenue, margin, retention, and lifetime value.
  • Build cohort and consumption-behavior analyses to identify expansion opportunity and churn risk.
  • Size segment opportunity and inform prioritization across self-serve, mid-market, and enterprise.
  • Surface customer concentration risk and account-level margin outliers to leadership.
  • Support pricing strategy with margin, elasticity, and competitive analysis across instance types and regions.
  • Model P&L impact of list price changes, discount structures, and committed-use constructs.
  • Support deal review on non-standard pricing, approval thresholds, and margin guardrails.
  • Track and report internal, non-revenue consumption of Public Cloud capacity across Engineering, Research, and demo or benchmark use.
  • Build and maintain allocation and chargeback methodology.
  • Quantify the opportunity cost of internal usage relative to external monetization.
  • Partner with Infrastructure Finance on capacity availability and utilization reporting.
  • Deliver Public Cloud insights for monthly business reviews, executive reviews, and board materials.
  • Translate consumption and operational data into a clear financial narrative.
  • Provide actionable recommendations to drive revenue growth, margin, and capital efficiency.

Desired Qualifications

  • Prior experience in a startup or high-growth organization, demonstrating adaptability and flexibility to thrive in such environments.
  • Experience with pricing strategy, unit economics, or deal desk support.
  • Working proficiency in SQL and comfort querying large data sets directly.
  • Experience with enterprise planning systems and CRM data.
  • Familiarity with GPU compute, AI/ML workloads, or public cloud infrastructure.

About the Company

Lambda

Cloud-based GPU services for AI training

Lambda Labs provides cloud-based GPU services for AI training and inference via its AI Developer Cloud, powered by NVIDIA GH200 Grace Hopper hardware to efficiently train large language models and generative AI. Customers access on-demand or reserved GPUs and are billed by the hour (for example, NVIDIA H100 at about $1.99/hour); it also offers Lambda Echelon, where clients own infrastructure but pay a monthly hosting and support fee, and Lambda Stack, a software suite that helps ML teams install and upgrade tools like PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA drivers. The company differentiates itself through competitive pricing, available high-end hardware, and an integrated ML software stack that streamlines workflows. Its goal is to enable scalable, affordable AI development and deployment by providing flexible GPU access, reliable hosting, and easy software deployment for teams working with large models.

Company size
501-1,000
Company stage
Late Stage VC
Total funding
$9.1B
Headquarters
San Jose, CA
Founded
2012

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