Senior Lead, AI Pricing & Margin Strategy

What You'll Do…
Cost & Margin Analysis

Analyze AI usage data — including API costs, token consumption, and inference spend — to build a clear picture of margin across customer-facing features and internal tooling.
Build and maintain financial models that translate AI usage into credit equivalents, helping the team track unit economics across model providers and feature surfaces.
Support the development of model routing frameworks by modeling the cost and quality tradeoffs of different routing configurations.

Pricing & Packaging Support

Support the design and evaluation of customer-facing credit and usage-based pricing models, including tier structures, usage limits, and overage mechanics.
Help translate pricing strategy into concrete SKU and entitlement definitions, working with Product and Billing to ensure commercial intent is correctly implemented.
Assist in structuring promotional offers and launch incentives for new AI features, modeling expected revenue and margin impact.

Cross-functional Collaboration

Partner with Finance, Data Science, Product, and Billing teams to gather inputs, align on assumptions, and ensure pricing decisions are grounded in accurate usage and cost data.
Prepare clear, well-structured analyses and presentations that communicate findings to stakeholders across the business.
Track and report on the commercial performance of AI features, flagging margin risks and opportunities as they emerge.

AI Market Research

Monitor the AI model landscape — tracking pricing changes, capability updates, and new providers across frontier labs — and surface relevant implications for Squarespace's strategy.
Research how competitors are packaging and monetizing AI capabilities to inform our own positioning and pricing decisions.

What We're Looking For…
2–4 years of experience in pricing strategy, commercial analytics, product finance, management consulting, or a related analytical role.
Strong quantitative and financial modeling skills — you're comfortable building models from scratch, stress-testing assumptions, and presenting findings clearly.
An interest in AI and a working familiarity with the LLM provider landscape (e.g., OpenAI, Anthropic, Google); you don't need to be a practitioner, but you should be genuinely curious about how these systems work and how they're priced.
Experience working cross-functionally with Product, Engineering, or Finance teams, and an ability to navigate ambiguity with structure and clarity.
Strong written and verbal communication skills; you can take a complex dataset and turn it into a clear, decision-ready summary.
A proactive, self-starter mindset — you're comfortable asking the right questions, defining scope, and driving work forward with limited direction.