Strategic Finance Lead
Rox
- Location
- San Francisco
- Employment
- Full Time
- Work model
- On-Site
- Level
- Senior
- Posted
- 2h ago
Skills
About this role
About Rox Rox is building the AI-native revenue operating system for modern go-to-market teams. Backed by Sequoia, GV, and General Catalyst, we replace fragmented CRM workflows with intelligent, autonomous systems that drive real outcomes. We are a Series A company, about 150 people, working at the intersection of data, AI, and revenue execution. Our platform supercharges sellers with autonomous revenue agents that take on the manual work, so people can focus on what they do best: selling. The same way coding agents 10x'd engineering, revenue agents are 10x'ing customer work. We are building the world's first revenue operating system, from the application layer down to the system of context underneath it. Humans move into the orchestrator seat, agents run the end to end customer lifecycle.
The Role
This is the first dedicated Strategic Finance hire at Rox, reporting to the Head of Finance. You will own the company operating model and be the analytical partner to the exec team on the decisions that move the business: how fast we hire, what we charge, which segments we lean into, and where margin comes from. Finance at Rox is an operating partner to Engineering, Product, and GTM, not a back-office function. The team is lean, builds most of its own tooling, and owns both revenue truth and margin truth. This is a lean unit with an outsized mission. The pace is high, the hours are long, and the surface area is wider than the headcount, which means real ownership, short turnarounds, and very little cover. You take the hard problems first, come back with a number that holds, and defend it in front of the exec team and the board. The scope grows with the company, and so does the opportunity to build a team around the function.
What You'll Own
The operating model: revenue, headcount, opex, margin, cash, and runway. The annual plan and the rolling reforecast, built so the exec team actually uses it The FP&A cadence: monthly forecast versus actuals across every department, variances explained in plain English, hiring and spend tradeoffs owned alongside budget owners Revenue truth: ARR, CARR, bookings, retention, and expansion defined once and used everywhere. Contracted, billed, and recognized revenue reconciled, with every delta explainable GTM finance: sales capacity and quota coverage, ramp and productivity by segment, pipeline analysis, and what the GTM plan implies for the P&L Unit economics and gross margin: margin by customer, segment, and product, including the AI and cloud COGS underneath it. CAC, payback, and LTV computed honestly, plus an efficiency pipeline with Engineering where margin is the constraint What You'll Support Pricing and packaging: the economics behind tier and pricing changes, discounting behavior, and the self-serve versus enterprise motion, so Product and GTM can decide with the tradeoffs quantified Board and investor materials: the board model and metrics package, plus clean, defensible numbers for fundraising and diligence What We Are Looking For 6+ years in strategic finance, FP&A, investment banking, private equity, growth equity, or consulting, with real ownership of an operating model High-growth B2B SaaS experience required, ideally including building something from scratch rather than inheriting a mature process Fluency in SaaS metrics: ARR, NDR, cohort retention, CAC payback, sales capacity and ramp. You should have opinions about how these get computed wrong Expert-level in spreadsheets, comfortable with large, messy datasets Concise, decision-ready writing that quantifies tradeoffs and makes accountability explicit Comfort with ambiguity and a bias toward building Based in the Bay Area, or willing to relocate, and in the San Francisco office five days a week Bonus Points Experience supporting a fundraise, board process, or diligence workstream Pricing and packaging work, especially across a self-serve and enterprise motion Working knowledge of AI and cloud cost drivers: LLM tokens, retrieval and