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ThoughtSpot

AI-powered analytics platform for data insights

Senior Manager of Financial Cloud & AI Operations

Corporate Finance & FP&AConfirmed live in the last 24 hours
Full-timeMountain View, CA, USA$154k - $220k /yr

About the Job

Requirements

  • At least 7 years of experience in financial modeling, driver-based forecasting, scenario analysis, and budgeting at an AI, SaaS, or other technology company.
  • At least 3 years of experience with cloud and AI economics, including working knowledge of cloud pricing and billing models such as reserved versus on-demand, commitments, and CUR exports.
  • Expert-level Excel proficiency and SQL knowledge, plus hands-on experience with BI tools, FinOps tooling, and cloud cost analysis tools.
  • Experience using or evaluating AI tools to improve forecasting or reporting.
  • Experience working on a globally distributed team.
  • Ability to simplify complex financial and technical topics and produce polished, executive-ready deliverables.
  • A bachelor's degree in Finance, Economics, or a technical field.
  • Ability to integrate AI into daily workflows to improve productivity and quality; hands-on experience leveraging AI tools, including industry-leading LLMs, to improve productivity, automate routine tasks, and improve work quality.
  • Experience using AI for research, content creation, and document summarization while retaining ownership of judgment and final decisions.
  • Ability to write effective prompts for AI tools.
  • Curiosity about AI tools, adaptability to emerging AI technologies, and critical judgment about when to use AI versus human judgment.

Responsibilities

  • Partner on forecast and scenario modeling for cloud, GPU, and token spending; connect usage, adoption, model mix, and vendor pricing to cost of goods sold and gross margin; monitor spending against budget, establish variance thresholds, and proactively address unplanned consumption spikes.
  • Partner with Engineering leadership to establish a unified demand forecast across production inference, internal research, and new product development, translating compute demand into prioritized spending and capacity plans.
  • Model and manage vendor commitments; evaluate break-even horizons, stranded-commitment risks, and optimal terms for Reserved Instances, Savings Plans, Committed Use Discounts, Provisioned Throughput Units, dedicated instance hosting, and token drawdowns; support contract negotiations with Procurement and Finance leadership.
  • Build unit economics models for AI products and serve as the finance partner to Product and Pricing, ensuring consumption-based pricing preserves gross margin targets.
  • Run allocation standards, controls, and review cadence that manage cost while enabling adoption; lead recurring reviews with the highest-consuming teams.
  • Define operational efficiency metrics with Engineering, including cluster and GPU utilization rates, cost per query, and cost per million tokens; track engineering cost-reduction commitments from initial sizing through billing impact, and escalate missed commitments to leadership.
  • Partner with Data Engineering and Business Intelligence to operationalize trusted multi-cloud datasets, automated allocation pipelines, and AI-driven anomaly detection; deliver executive-ready financial reporting, risk assessments, and strategic recommendations to the CFO, CTO, and executive leadership.
  • Experiment with ThoughtSpot AI tools and leading industry LLMs to streamline workflows, enhance output, and uncover insights.

About the Company

ThoughtSpot

AI-powered analytics platform for data insights

ThoughtSpot provides analytics and business intelligence tools that help people analyze data quickly. Its core product is an AI-powered analytics platform that lets users ask questions in plain language and get fast insights from large data sets. It also offers ThoughtSpot Embedded, which lets developers add analytics directly into their apps. The company uses a subscription-based model and offers services like training and support. ThoughtSpot differentiates itself with fast, AI-driven analytics that can turn large data collections (for example, Excel data spanning 45 days) into actionable insights in minutes, plus a strong community of users and developers that shares knowledge and skills. Its goal is to help organizations become data-fluent and make better decisions by making analytics easy to access and act on.

Company size
1,001-5,000
Company stage
Series F
Total funding
$643.7M
Headquarters
Mountain View, CA
Founded
2012

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