Pricing and Monetization Manager

Mercor•San Francisco, CA

2d•$190,000 - $250,000•Onsite

About The Position

Mercor is hiring a Pricing and Monetization Manager to own pricing structures across its data types for AI labs and enterprise customers. The role will build pricing models, rate cards, and margin frameworks to make pricing consistent and defensible. It covers both acquisition pricing and cost modeling for data Mercor buys, and monetization and margin strategy for data it sells. The position works closely with Sourcing, Sales, Finance, and Product. Mercor works in person five days a week in its San Francisco, New York City, or London offices.

Requirements

At least 6 years of experience in pricing strategy, monetization, revenue operations, or a related analytical or strategy role.
Strong financial and quantitative modeling skills.
Fluency in Excel or Sheets.
Ability to build pricing models from scratch.
Comfort operating amid ambiguity and applying structured judgment when public pricing benchmarks are limited.
Strong cross-functional communication skills and ability to work daily with Sourcing, Sales, Finance, and Product.

Nice To Haves

Experience at a data, marketplace, or B2B SaaS company.
SQL skills.
Experience pricing a two-sided marketplace with both a cost/supply side and a demand/sell side.
Background in AI training data or market data.

Responsibilities

Establish fair market value for acquired data by data type, domain, and end buyer, and set acquisition cost ceilings.
Track competitor bidding behavior and maintain cost curves to determine when to increase bids to win a corpus and when to walk away.
Model fully loaded cost to serve, including acquisition, extraction, human quality control, anonymization and PII handling, and LLM inference.
Define the priceable unit for each data type and the quality attributes that affect pricing within each unit.
Design pricing and packaging frameworks for data by type, volume, format, and customer segment.
Build rate cards and tiered pricing for recurring, repeatable data products.
Own unit economics per deal and data product, including transfer pricing, internal chargeback, and direct versus indirect revenue attribution across account P&Ls.
Build the pricing and forecasting toolkit Finance and leadership use to evaluate data products before launch.
Continuously test and refine pricing based on win/loss data, renewal behavior, and market feedback.