Pricing Product Manager

The Pricing Product Manager is a strategic role responsible for defining the vision, roadmap, and value of the company’s pricing products, while iteratively adapting to customer feedback and market changes. This role owns the performance of all pricing methods across all merchandise categories, measured by sales performance (margin, sales velocity, inventory turns), pricing indicators (penetration, acceptance, average price), and field satisfaction with pricing.AI is foundational to this role: the Pricing Product Manager partners with Data Science to deliver AI-driven pricing models, Gen AI-powered data cleanup (aliasing and MDM), and pricing experimentation infrastructure, and champions AI adoption across the pricing function. This role leads the strategy of HOW we price and triggers WHEN pricing methods need to be optimized.
This role involves close cross-functional collaboration with Data Science, Pricing Analysts, Data Engineering, Earning Assets, field operations, and executive leadership to identify pricing opportunities, validate them through controlled experiments, and operationalize pricing improvements at scale. The Pricing Product Manager owns pricing strategy; the Pricing Analysts own pricing diagnostics and root-cause analysis, feeding validated insights into this role's decisions.
ESSENTIAL DUTIES & RESPONSIBILITIES:Own the performance of all pricing methods across all merchandise categories against sales, pricing, and field-satisfaction KPIs.Lead the strategy of HOW we price: which categories need direct input pricing (DIP prices) tied to retail prices, which parameters drive the Historic Pricing method, which manufacturers and models are covered in the product master data (MDM), and which categories require price buffers.Trigger WHEN pricing methods need to be optimized, based on performance signals and early-warning indicators.Define the vision, roadmap, and requirements for AI-driven pricing products — translating model performance into business outcomes and championing AI adoption across the pricing function.Partner with Data Science to build and operationalize pricing experimentation infrastructure — pilots, A/B tests, trigger logic, and monitoring — including the introduction of a partner-developed AI pricing model (Scoop).Drive automation and data quality across the pricing pipeline with Data Science and Data Engineering — replacing manual processes with scalable, AI-assisted systems and leveraging Gen AI/LLM product matching to improve the product master data (MDM), aliases, and pricing parameters.Build the analytical methodology, trigger logic, and response playbook alongside the Pricing Analysts — who own pricing diagnostics and root-cause analysis — then own the trigger-to-action workflow: classify pricing issues (data anomaly, aliasing, MDM, or methodology), recommend a fix, estimate the impact, and communicate it to business stakeholders (Earning Assets).Guide the integration of external and competitive prices into pricing methods to keep prices market-aligned.Ensure stable prices during transitions and manage the risk of pricing releases through pre- and post-release analysis.Prioritize the pricing backlog, balancing immediate enhancements against strategic builds.Communicate pricing performance, planned changes, and expected business impact to business and executive stakeholders, guiding data-driven pricing decisions and AI adoption.
EDUCATION & EXPERIENCE:Bachelor’s degree in Business, Economics, Engineering, Computer Science, or related field; MBA or advanced degree preferred.7+ years of product management experience, including ownership of data-driven or AI-powered products.Proven experience partnering with data science teams to build and ship ML, AI, or Gen AI products end-to-end.Technically hands-on: working SQL and Python — able to self-serve analysis, query data directly, and validate model outputs.Demonstrated practical use of AI tools (Claude Code, Claude, or similar LLM tools) in daily work — AI fluency is critical to how this team operates.Experience in pricing, revenue management, or merchandising analytics strongly preferred.Experience designing and running experiments (A/B tests, pilots, monitoring) to validate product changes.Retail, resale, e-commerce, or pawn industry experience a plus.