Pak Shing Ho

Applied Scientist Mle Economist @Linkedin

San Francisco, CA, US

I develop end-to-end machine learning and causal inference systems that combine experimental and observational data to optimize the trade-offs between rapid short-term experimentation and long-term strategic outcomes across a global user base of over 1 billion.• Build scalable causal ML & interpretable models to evaluate high-value user actions on key business metrics for 1B+ users.• Optimize A/B testing experimentation using the mixed of experimental and observational data and surrogates together with long-term outcome causal machine learning models to reduce dependence on extended experiments.• Embed time series outputs into operational workflows to inform leadership decisions and resource planning.• Lead cross-functional collaboration across Data Science, Product, and Finance to translate forecasts into action.Keywords: Machine Learning, causal inference, experimentation, time series forecasting, long-term uplift, engagement, retention, KPI, Python, Flyte.

Barcelona School of Economics

Master's degree, Economics and Finance

Universitat Pompeu Fabra

Doctor of Philosophy - PhD (transferred to Boston University), Economics, Finance and Management

University of Melbourne

Bachelor of Commerce - BCom, Economics, Mathematics & Statistics

Harvard University

Visiting Ph.D student, Economics

Boston University

Doctor of Philosophy - PhD, Economics

UNSW

Commerce

The London School of Economics and Political Science (LSE)

MSc (Research), Economics