Quantitative Strategist Lead

Company Description

LETSGO is redefining how collectors open packs. Users reveal real, authenticated Pokémon and sports cards digitally, then choose whether to sell them back instantly, ship them home, or store them securely in their vault.

Behind this simple experience is a sophisticated quantitative system connecting physical assets, digital inventory, changing market prices, pack economics, probability, liquidity, and user behavior.

Founded in 2025, we’re a team of geeks and collectors ourselves. We’re passionate, ambitious, and determined to lead this exciting new category as it takes shape.

As the Quant Strategy Lead at LETSGO, you’ll build and own the mathematical systems that power our core business and shape both revenue and user experience.

You’ll develop the quantitative frameworks behind pack economics, RTP and payout distributions, dynamic pricing, buyback, card acquisition, and physical and digital inventory allocation.

The role sits at the intersection of mathematics, technology, product, operations, and business strategy. You’ll solve complex problems involving constrained optimization, probability, asset valuation, inventory risk, and user behavior, then turn your models into production systems and critical business decisions.

This is not a traditional analytics, data science, or operations role. You won’t simply analyze an existing system. You’ll help design and continuously evolve the mathematical foundation on which the entire business operates.

This is a rare opportunity to build a critical quantitative function from the ground up, with significant ownership, fast feedback loops, and direct impact on both the product and the business.

What you’ll do:

• Own the quantitative framework behind LETSGO’s business, from physical card acquisition to the user’s pack opening and buyback experience.

• Design and optimize pack economics, including pack composition, card allocation, price ranges, probabilities, RTP, payout distributions, hit rates, and volatility.

• Build purchasing and inventory models that determine which cards to acquire, at what price, in what quantities, and when to replenish them.

• Model the relationship between physical assets and their digital inventory, including availability, allocation, exposure, circulation, and inventory constraints.

• Develop dynamic valuation, pricing, and buyback models that respond to card values, acquisition costs, liquidity, supply, demand, and inventory levels.

• Research, simulate, and stress test alternative system designs, identifying risks, edge cases, and unintended outcomes before they reach production.

• Identify high impact quantitative problems and own them from initial formulation through implementation and measurable results.

• Partner closely with product, engineering, operations, and leadership to translate complex models into scalable systems, operational rules, and strategic decisions.

• Monitor actual outcomes against model expectations and continuously refine the system as market conditions, inventory, demand, and user behavior evolve.

• Bachelor’s or advanced degree in mathematics, statistics, computer science, physics, data science, operations research, financial engineering, or another highly quantitative field.

• Exceptional depth in at least one quantitative discipline, such as probability, statistics, optimization, simulation, algorithms, or applied machine learning.

• Strong strategic thinking and the ability to model complex systems in which pricing, inventory, probability, capital allocation, and user outcomes interact.

• Proficiency in Python and SQL, with the ability to independently explore data, build simulations, test hypotheses, and validate models.

• Ability to turn open business questions into rigorous mathematical frameworks and practical production decisions.

• Strong product and commercial judgment, including an understanding of how quantitative decisions affect both user experience and business performance.

• Comfortable independently building and leading a critical quantitative function, with the judgment to make high impact decisions with limited oversight.

• Strong communication skills and the ability to explain complex quantitative concepts clearly.

Preferred:

• Prior experience as a quantitative trader, quantitative researcher, pricing or risk strategist, or in another role involving complex probabilistic systems.

• Experience with dynamic pricing, asset valuation, constrained optimization, probabilistic allocation, inventory risk, liquidity, or payout systems.

• Experience in trading, marketplaces, gaming mathematics, collectibles, or businesses involving physical assets with changing market values.

• A strong record of quantitative excellence, such as an advanced degree, competitive mathematics, physics, or computer science, or exceptional achievement in another analytical discipline.