Stefan Ruffini

• ›Own creator-facing performance-driven merchandising and discovery tooling within Fortnite Discover, translating creator-provided metadata and configurations into dynamic player-facing experiences.
• ›Define how creator inputs (metadata, configurations, variants) are consumed and interpreted by Discover in partnership with the Creator Portal team.
• ›Own the presentation logic that adapts how content appears based on experimentation results, performance signals, and player engagement.
• ›Built and scaled A/B testing and experimentation capabilities evaluating merchandising effectiveness, tripling experimentation throughput.
• ›Introduced standardized experimentation frameworks, including an experiment power calculator, improving test reliability and reducing underpowered experiments.
• ›Designed performance reporting that identifies which merchandising strategies drive engagement, enabling faster iteration and clearer decisions.
• ›Make prioritization and trade-off decisions across competing initiatives in a resource-constrained environment.
• ›Serve as the subject-matter expert for merchandising performance and experimentation, supporting requirement discovery, feature rollout, and adoption.

• ›Owned experimentation ecosystem powering 200+ annual tests optimizing personalization and engagement.
• ›Designed data pipelines reducing analysis time by 90% and saving over $2M annually.
• ›Led near real-time performance data initiatives driving $50M+ monthly revenue and improving discovery efficiency by 10%.
• ›Established experimentation governance frameworks reducing data error rates by 5%.
• ›Mentored PMs and analysts, improving onboarding time and delivery consistency.

• ›Built real-time dashboards enabling predictive analysis and ad performance optimization across markets.

• ›Delivered consumer analytics improving media personalization and campaign optimization.
• ›Automated reporting workflows, cutting production time by 75%.
• ›Created dashboards that drove a 40% uplift in ad revenue.

Designed and built an AI-driven trading system applying experimentation science and adaptive systems design. The platform uses multi-armed bandit logic and Darwinian-style strategy evolution to optimize decisions over time. Integrated sentiment analysis (Reddit, Yahoo Finance, Google News) and rolling volatility signals to inform probabilistic thresholds. Implemented modular signal logging, dynamic mutation scoring, and long-tail sentiment decay in Python.