Commercial Finance Lead

Responsibilities

• Own the forward-looking commercial model and bottoms-up forecast, including its data, methodology, and assumptions across pipeline, conversion, customer usage, retention, expansion, sales capacity, and timing.
• Provide FP&A with the commercial outlook and operating assumptions used to forecast revenue, spending, margin, and cash.
• Build complex cohort and financial dashboards covering acquisition channels, customer profiles, conversion, usage, churn, expansion, and unit economics.
• Identify leading indicators, pressure-test growth targets, and flag risks before they appear in financial results.
• Analyze performance against plan and translate underlying drivers into risks, opportunities, and recommended actions.
• Evaluate investments across sales capacity, marketing programs, customer expansion, partnerships, and new routes to market.
• Support pricing, promotions, and discounting decisions through analysis of customer value, revenue quality, consumption behavior, and margin.
• Partner with Revenue Operations, Sales, Marketing, Customer Success, and FP&A to align assumptions, set targets, identify investment needs, and drive action.
• Prepare clear, executive-ready materials for leadership and Board discussions, communicating conclusions, assumptions, and recommendations in an easily digestible manner.
• Apply emerging AI technologies to accelerate analysis, improve forecasting, and expand capacity for high-leverage work.

Requirements

• 6+ years of experience across Commercial Finance, business operations, strategy, sales, marketing, or data roles in a SaaS or technology company.
• Understand consumption-based models and how pipeline, conversion, usage, retention, and expansion drive revenue.
• Built detailed operational forecasts and can translate commercial activity into a forward-looking growth outlook.
• Highly analytical, with strong financial modeling, Excel, BI, and data skills.
• Understand SaaS and cloud economics, including gross margin, retention, expansion, CAC, payback, lifetime value, and sales productivity.
• Can connect fragmented data across commercial, product, and financial systems into a coherent view of the customer and business.
• Look beyond lagging results to identify the leading indicators most predictive of future performance.
• Can challenge assumptions, align senior leaders, and influence without direct authority.
• Comfortable moving between detailed analysis and C-suite discussions.

Nice-to-Haves

• Experience applying emerging AI technologies to accelerate analysis and improve forecasting.
• Enthusiasm for learning new technologies.