Staff Pricing Data Scientist
Confirmed on the employer's own hiring board on Oct 22, 2026. First seen by Alion on Oct 22, 2026.
Staff Pricing Data Scientist, Partnerships
New York, NY (USA)
1. About the Opportunity
Join our Partnerships team as Staff Pricing Data Scientist and help shape how MoonPay prices its products for partners. As our suite grows across on-ramp, off-ramp, swaps, crypto deposits, and virtual accounts, pricing has become one of the biggest levers on partner revenue and win rates.
You'll help set and adjust partner-specific pricing across individual products and multi-product bundles, and build the tools that make pricing faster, more consistent, and more transparent for the whole team.
2. What you will do
Develop and execute pricing strategy for individual products and multi-product bundles, including tier structures, concessions, and waivers.
Make partner pricing changes directly, within agreed guardrails, and document the rationale behind each change.
Review existing pricing using partner-level volume, conversion, and cost data to find where we're leaving revenue on the table or pricing ourselves out of deals.
Build AI-powered tools for the Partnerships team, such as pricing calculators, deal-sizing models, transparency dashboards, and experimentation frameworks.
Partner with Finance, Partnerships, Product, Compliance, and Legal so pricing is competitive, commercially sound, and consistent with contract terms and cost structure.
Reduce ad-hoc pricing escalations by building clear frameworks and self-serve guidance that the partnerships team can use with confidence, and flag product changes the pricing data suggests.
3. About You
Deep experience in pricing, monetization, or commercial analytics, ideally at a payments, fintech, marketplace, or B2B platform business.
Strong SQL and Python (or similar), and comfort working with large transactional datasets.
Experience designing pricing structures such as tiering, bundling, volume discounts, and take-rate models, with a commercial mindset that ties unit economics and competitive context to a clear recommendation.
AI fluency: you're comfortable building internal tools with AI assistance and iterating quickly.
Ability to influence senior stakeholders, act with autonomy, and explain complex pricing logic to non-technical teammates and partners.