FP&A AI Architect

Gusto’s Finance team is rebuilding planning, forecasting, and analysis with AI at the center. As Staff FP&A AI Architect, you’ll design and lead the technical and process architecture that turns FP&A from a manual, spreadsheet-driven cycle into an automated, insight-generating function. You’re a hybrid: deep enough in finance to understand driver-based planning, variance analysis, and board reporting, and deep enough in data and AI to design the systems that automate them. You’ll partner with the CFO org, FP&A, Accounting, Data, and Engineering.

This is a new role on the Enterprise AIT team, which drives the intelligent transformation of Gusto’s enterprise systems by integrating AI, automation, and advanced analytics across internal applications. The team works closely with Finance, Business Ops, IT, and Security, and this role brings dedicated AI architecture capacity to FP&A and the CFO org.

• Set the strategy: Define the multi-year architecture and roadmap for AI-enabled planning, forecasting, and reporting, prioritizing use cases by value, feasibility, and risk.
• Build forecasting systems: Architect driver-based, ML-assisted forecasts for revenue, headcount, opex, and cash, combining statistical models with LLM workflows and benchmarking against human forecasts.
• Automate analysis: Build agentic workflows for variance commentary, flux analysis, scenario modeling, board and QBR narratives, and ad-hoc “ask the numbers” questions.
• Own the finance data foundation: Work with Data Engineering on governed data models connecting the GL, billing, CRM, HRIS, and planning tools, so AI outputs are traceable and auditable.
• Embed controls and trust: Establish evals, human-in-the-loop checkpoints, lineage, and audit trails that meet SOX, security, and privacy requirements.
• Evaluate and select tools: Drive build-vs-buy decisions across planning platforms, LLM providers, and orchestration frameworks.
• Lead through influence: Mentor analysts and engineers, create reusable patterns and templates, and drive AI adoption across Finance.
• Measure impact: Report on cycle-time reduction, forecast accuracy, analyst capacity freed, and decision latency.

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