Strategy, Frameworks & Leadership

Scalable approaches to Trust, Safety, and Growth Engineering.

About me

Hello, my name is Steve Chua. I am currently working at Google in the Bay Area. I graduated from the University of California-Berkeley with a an MBA & Masters in Operations Research. In the past, I have worked for both established companies and smaller companies, including Facebook, Google, PayPal, and AT&T. I help manage large programs as well as drive data-driven culture in technology.

Mental Models for Scalable Data Science

1. Trust & Safety Strategy

How I build systems that protect platforms without hindering growth.

  • Friction-Fraud Balancing: I implement automated identity verification that targets high-risk actors while maintaining a seamless experience for legitimate users, resulting in 50%+ fraud reduction.

  • Layered Defense: My approach combines deterministic rules (Risk Tiering) with probabilistic models (Anomaly Detection) to catch 70-80% of fraud.

  • Automated Response: I focus on "Time-to-Action," reducing manual review times from hours to minutes through intelligent routing and automation.

2. Revenue & Growth Engineering

Driving profitability through predictive analytics and ROI optimization.

  • Customer Health Scoring: I developed weighted frameworks (e.g., usage growth, stability, and engagement) to identify $300M+ in incremental revenue opportunities.

  • Marketing Efficiency: By designing web measurement and spend frameworks, I've enabled teams to double their ROI on marketing spend through data-backed resource allocation.

  • Developer Ecosystems: I led API externalization and platform service projects that unlocked $100M+ in developer revenue.

3. Data Science Excellence (The "How")

Standardizing the methodology for global-scale impact.

  • Experimentation Frameworks: I build identity verification and product onboarding experiments that systematically reduce policy abuse by 30%.

  • Model Monitoring & Drift: I implement automated monitoring frameworks to track model performance in real-time, ensuring long-term revenue growth and integrity.

  • Feature Engineering for Imbalance: Specialized experience in handling highly unbalanced datasets (e.g., payments fraud) where signal-to-noise ratios are extreme.

4. Leadership & Execution

Bridging the gap between engineering, product, and the C-Suite.

  • Strategic Alignment: I facilitate executive-level discussions to align technical data roadmaps with company-wide profitability goals.

  • Operational Scale: I reduce management overhead by developing reporting frameworks that save 40+ hours of manual work per month.

  • Technical Product Management: Leveraging my MBA training at UC Berkeley Haas to lead complex network deployments and software developments with 40% lead-time reductions.