Guang Ouyang

Fractional ML Infrastructure Engineer (Ex-Google)

I help teams fix slow, unreliable data pipelines and improve ML system performance in production.

Typical Problems I Solve

• Data pipelines that break or take too long to run
• ML models that work offline but fail in production
• SQL queries and dashboards that don’t scale
• Systems that are too complex or expensive to maintain

Selected Impact

Former Data Scientist at Google. Improved production ML models through experimentation, feature engineering, and pipeline optimization — contributing to ~$400M annual revenue impact.

• Built and deployed production ML systems in C++ under strict latency constraints
• Designed reusable ML pipeline frameworks to accelerate iteration
• Eliminated recurring production failures via automated validation systems

Engagement Options

• Free 30-min consultation
• Pipeline / architecture audit (1–2 weeks)
• Performance & cost optimization
• ML system production readiness assessment

Axiom Data Lab

In parallel, I am building a quantitative research system focused on large-scale financial data pipelines and predictive modeling for long-horizon investment strategies.