Quant Strategist
US$200,000 – US$400,000 / yr base salary
Posted 12 Mar 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
| Base salary | US$200,000 – US$400,000 / yr |
|---|
Field of studyComputer Science, Data Science, Mathematics, Physics (or related)
Study levelBachelor's
Field of studyQuantitative discipline
Study levelMaster's or higher
The Role
As a Quant Strategist, you'll sit at the intersection of mathematics, computer science, and core business decisions. You'll have a direct impact on all facets of the business, applying your quantitative rigor directly to what drives revenue and player experience. You'll build the models and frameworks that power our most critical business decisions.
You'd be joining a small, high-output quant team (4 people today) that operates more like a trading desk than a data org. We build the mathematical systems that power Triumph's core business: pricing engines, payout distributions, matchmaking algorithms, risk models, and player behavior systems.
The team is led by a former quant trader, and current members left careers in quantitative trading to be here. The problems are as deep and interesting as what you'd find at a quant fund - constrained optimization, adversarial dynamics, risk - but with more ownership, faster feedback loops, and direct impact on what gets built. You ship something, and you see it in the numbers the next day.
What You'll Do
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Develop and optimize core risk-taking and revenue-generating algorithms — the pricing engines, payout structures, and edge calculations that are the mathematical backbone of Triumph's business.
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Own the quantitative framework for Rips by Triumph: pricing models, pack economics, and rarity calibration.
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Design and analyze experiments (A/B tests and beyond) with rigorous statistical methodology.
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Identify high-leverage quantitative problems across the business and drive them from formulation to production impact.
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Partner closely with engineering, product, and leadership to translate model outputs into real-time production systems and strategic decisions.
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Design and build machine learning models that directly inform acquisition spend and product decisions.
Qualifications
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Bachelor's degree in a quantitative subject: math, physics, computer science, data science, or a related discipline.
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True depth and mastery over any quantitative domain: probability, statistics, applied machine learning, computer science, or math. We want spiky people who are confident that they are the best in their discipline.
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Proficiency in Python and SQL.
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Experience working with large amounts of data.
Preferred
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Prior experience as a quantitative trader or quantitative researcher.