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Salary: USD 130,000 - 180,000 / annual
Snorkel AI is a frontier AI data lab founded out of Stanford in 2019, helping teams build datasets, benchmarks, and evals for high-performing AI systems. The company works with leading AI labs and enterprises to move from better data to better outcomes.
Reporting to the Senior Director of Finance, the DaaS Finance Manager is a critical member of the Finance organization and dedicated finance partner to the DaaS (Data-as-a-Service) business as it scales. This role bridges finance rigor with operational execution across Sales, Delivery Ops, and RevOps.
Key responsibilities span three pillars:
**Revenue Model & Forecast:** Maintain the DaaS revenue model covering bookings, acceptance-based revenue, and ARR by deal with scenario cases. Partner with RevOps on weekly revenue and ARR forecasts, running the weekly forecast deck for leadership. Reconcile field forecasts (commit, forecast, best case) with deal-by-deal finance views; quantify coverage, swing deals, and risks. Track delivery pacing and acceptance against plan. Build variance analysis for quarterly and annual planning. Develop DaaS inputs to the annual operating plan including revenue, cost of delivery, margin, and delivery headcount with monthly actuals-vs-plan analysis. Partner with Accounting on revenue recognition, COGS, and margin actuals.
**Delivery Capacity Model:** Run and maintain the delivery capacity model in partnership with Delivery Ops, covering utilization, project mix, headcount per project, and cost to deliver. Reconcile capacity plans with actual staffing data and explain variances. Translate the bookings pipeline into staffing demand and flag hiring needs ahead of quarters. Lead regular capacity planning reviews with Delivery leadership.
**Business Insights, Pricing & Strategic Projects:** Surface trends in pricing, mix, margin, and pacing with clear recommendations. Partner with Deal Desk on deal economics, reviewing deal models and pricing templates for accuracy and margin. Analyze customer and product profitability (cost to deliver by customer, project, and product tied to revenue). Determine what it takes for each product to hit margin targets. Identify and drive fixes to broken or slow processes. Automate recurring models from manual spreadsheets to scalable, auditable tools. Support board and investor content for the DaaS business.
Success milestones: By 30 days, learn the DaaS model end-to-end and co-produce one weekly forecast deck. By 60 days, run the weekly forecast independently and lead monthly capacity reviews. By 90 days, recommend fixes to pricing templates, reconcile capacity models, and deliver customer/product profitability views. By year-end, achieve trusted forecast accuracy, refresh margin and capacity assumptions quarterly without fire drills, and automate recurring models.
**Requirements:**
Must-haves:
- 5–8 years in FP&A, product or business finance, deal desk, or strategic finance; at least 2 years at a high-growth tech company
- Hands-on ownership of a revenue forecast and a unit-economics or pricing model (not just review)
- Fluency in gross margin vs contribution margin, cost-to-serve, and how pricing, discounting, and incentives flow into both
- Experience with services or labor-based delivery economics: utilization, headcount per project, contractor mix
- Expert spreadsheet modeling (Google Sheets/Excel) and working SQL; comfortable pulling from CRM and data warehouse
- Clear, concise executive communication; can write a five-line summary leadership can act on
- Comfort with ambiguity; makes reasoned calls with incomplete data, states assumptions, and refines as facts arrive
- Process mindset; spots friction, proposes simpler ways, and drives adoption
- Bachelor's degree in finance, economics, engineering, or related field
Nice-to-haves:
- Experience in AI/ML, data services, marketplaces with reusable inventory, services, or manufacturing
- Familiarity with ARR-style metrics and acceptance-based revenue recognition
- Board or investor materials experience, including fundraise data rooms
- Uses AI tools and light scripting (Python) to automate recurring analysis
- Experience with BI tools (Looker, Tableau) or planning tools (Adaptive, Pigment)
- Background at top consulting, banking, or finance-rotation program
