Data Scientist

Triumph•San Francisco, CA

11d•Onsite

About The Position

As a Data Scientist, you'll own the quantitative systems that drive how millions of real-money players experience Triumph's products, from their first session to long-term retention and monetization. You'll build the models and frameworks behind our most critical business decisions: how we price, how we pay out, how we match players, and how we grow. You'd be joining a small, high-output quant team (4 people today) that operates like a trading desk. We build the mathematical systems that power Triumph's core business: pricing engines, payout distributions, matchmaking algorithms, risk models, and player behavior systems. Every model we ship touches real money and real users. You see the impact in the numbers the next day.

Requirements

Bachelor's degree in a quantitative subject: math, physics, computer science, statistics, economics, or a related discipline.
True depth and mastery in at least one quantitative domain: probability, statistics, applied ML, causal inference, or mathematics. We want spiky people who are confident they are among the best in their discipline.
Proficiency in Python and SQL.
Experience working with large-scale user or behavioral datasets.

Nice To Haves

Experience in consumer tech, gaming, fintech, or marketplace data science, particularly in monetization, LTV modeling, or experimentation.
Prior experience as a quantitative trader or quantitative researcher.
Experience in competitive math, physics, or CS olympiads, or a graduate degree in a quantitative discipline.
Nationally competitive in any activity. Some members of our team include national champions in debate, Clash Royale, and Poker.

Responsibilities

Monetization & Pricing: Develop and optimize the pricing engines, payout structures, and edge calculations that are the mathematical backbone of Triumph's revenue. Own pack economics, rarity calibration, and pricing models for Rips by Triumph.
User Journey & Retention: Build models that map the full player lifecycle: acquisition, activation, engagement, monetization, churn risk. Identify the quantitative levers that move retention and LTV, and design interventions that act on them.
Experimentation: Design and analyze experiments (A/B tests and beyond) with rigorous statistical methodology. Own the measurement framework that tells us what's actually working across the product.
Behavioral Modeling: Develop ML and statistical models on rich, high-frequency user behavior data (session patterns, spend curves, matchmaking outcomes, gameplay trajectories) to drive both product decisions and real-time production systems.
Growth & Acquisition: Build models that directly inform acquisition spend and channel optimization, connecting upstream marketing decisions to downstream LTV and monetization outcomes.
Cross-Functional Impact: Partner closely with engineering, product, and leadership to translate model outputs into shipped features and strategic decisions. Identify high-leverage quantitative problems across the business and drive them from formulation to production impact.