Senior Product Manager, Pricing

About Keystone AI Keystone AI is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $100M in annual bookings. We're expanding beyond lawn care to become the one-stop shop for all home services — operating across three brands (Keystone AI, Lawn Love, Home Gnome) on a single shared platform. About Pricing at Keystone AI Upfront pricing is our competitive moat. Most home service marketplaces make you find a Pro and wait for a custom quote. Keystone AI gives customers a price immediately and assigns a Pro. That's a huge differentiator — but it means we have to get pricing right at scale for services where the industry norm is custom quotes for everything. That tension — seamless upfront pricing vs. inherently custom work — is what makes this domain both critical and uniquely challenging. Today, pricing is split across three fragmented systems with no dedicated owner. The Role You'll own the full pricing and monetization domain — from the infrastructure that powers every price we show to the strategy that determines what we charge, how we bundle, and where we expand. This is a transformation role. We're migrating from three disconnected pricing paradigms to a unified dynamic pricing system. The roadmap has 11 priorities, a dedicated engineering team, and no full-time product owner. What makes this role different: You own the entire pricing domain : Not a slice of pricing alongside other PM work. Pricing infrastructure, dynamic pricing strategy, new revenue models, service availability — all yours. Strong data team partnership : A data team owns pricing models and analytics. You define what to optimize. They figure out how. You don't build models — but you speak the language fluently. Infrastructure-first sequencing : The big pricing gains require a new pricing API and data product first. You need to be the PM who gets energized by building the foundation, not frustrated by it. What You'll Own Dynamic pricing roadmap : Migrate three legacy pricing systems to a unified pricing service. Sequence the 11-priority roadmap, make tradeoff calls, and ship. Pricing strategy : Define pricing for 24+ services across mowing, non-mowing, and emerging verticals. Balance conversion, margin, and Pro economics. New revenue models : Unlock bundles, add-ons (e.g., "bag my clippings"), channel discounts, and frequency-based pricing — none of which exist today. Service availability : Determine where and when we offer services based on supply, demand, and profitability signals. Experimentation framework : Stand up A/B testing for pricing changes, measuring impact on conversion, margin, and Pro claim rates. Problems to Solve Three pricing systems that can't talk to each other. Pre-priced tables (1.8M+ rows of static lookups), instant quote logic (hardcoded per-service rules across APIs), and manual Pro quotes. None can combine location + frequency + brand + supply-demand into one decision. You'll architect the migration to a unified system without breaking pricing that 100K+ customers rely on today. Mowing is 90%+ of revenue and stuck on static pricing. Our biggest service can't apply dynamic variables like supply tightness, seasonal demand, or channel discounts. A $3 price change swings conversion by ~10%. You'll partner with the data team to build pricing intelligence into mowing without destabilizing the core business. 24+ services need to migrate, each one different. Bush trimming, pool cleaning, landscaping, leaf removal — different pricing variables, ordering flows, customer expectations. You'll define the migration sequence and determine which services get dynamic pricing vs. simplified models. No bundles or add-ons exist. Customers can't bundle services for a discount or add options to their mowing — a major untapped revenue and retention opportunity. You'll design the pricing architecture that makes bundling possible. What Success Looks Like (Year 1) Pricing API/service shipped and live — Replaces at least one legacy paradigm with a clear path to consolidating the others Mowing on dynamic pricing — Core service running on the new system with measurable margin or conversion impact Experimentation framework operational — Team can A/B test pricing changes and measure impact within days, not months Bundle/add-on architecture defined — System design complete and engineering building, even if not yet live Who You Are AI-native. You use AI daily — scenario modeling, pricing analysis, data exploration, drafting specs. You push AI into parts of your workflow others haven't thought of yet. This is unlikely to be a good fit if you view AI as a novelty rather than a core productivity lever. A systems thinker who architects platforms, not just sets prices. You see pricing as a system: inputs, rules, feedback loops, edge cases. You can design a pricing architecture that handles 24 services across 3 brands with different economics — and explain it to an engineer in a way they can build. This is unlikely