About the job
Join Our Dynamic Team
Triumph Arcade is revolutionizing mobile gaming by enabling players to wager and win real money, engage in large-scale multiplayer experiences, and participate in social tournaments. As we continue to soar with exponential growth, our top-rated app in the App Store is a testament to our innovation. We're on the brink of launching exciting new products like Rips, a collectibles app that has already achieved significant success, and the best is yet to come.
Triumph Arcade is supported by leading consumer venture capital firms such as Goodwater Capital, General Catalyst, and DraftKings Drive Fund, ensuring our growth and innovation continue unabated.
Your Impact
As a Data Scientist, you will spearhead the quantitative systems that shape the experiences of millions of real-money players using Triumph's offerings. From their first gaming session to long-term retention, your analytical expertise will inform critical business decisions regarding pricing, payouts, matchmaking, and growth strategies.
You will collaborate with a compact yet high-performing quantitative team that operates like a trading desk, developing the mathematical frameworks that underlie Triumph's business model: pricing engines, payout distributions, matchmaking algorithms, risk models, and player behavior systems. Your contributions will have a direct impact on real money and real users, with measurable results visible in the metrics almost immediately.
Responsibilities
Monetization & Pricing: Create and refine the pricing engines, payout structures, and edge calculations that form the financial foundation of Triumph’s revenue. Manage pack economics, rarity calibration, and pricing models for Rips by Triumph.
User Journey & Retention: Develop models that analyze the entire player lifecycle: acquisition, activation, engagement, monetization, and churn risk. Determine the quantitative factors that enhance retention and lifetime value (LTV) and design strategies to leverage them.
Experimentation: Conceptualize and evaluate experiments (A/B tests and beyond) using robust statistical methods. Own the measurement framework to assess what strategies are effective across our product suite.
Behavioral Modeling: Build machine learning and statistical models based on rich, high-frequency user behavior data, including session patterns, spending trends, matchmaking results, and gameplay trajectories.

