AI & Data Science Manager · Full-Stack ML Engineer · Available for Select Consulting
Hi, I'm Mark—an AI & Data Science Manager and Software Developer. I architect machine learning pipelines, demand-forecasting engines, and full-stack applications that drive commercial decisions.
BS in Applied Mathematics
Ateneo de Manila University
Summa Cum Laude · Program Award
3.92/4.00 QPI
Master in Data Science
Ateneo de Manila University
4.00/4.00 QPI
Minor in Enterprise Systems
Ateneo de Manila University
Completed
Available for select freelance, consulting, and technical advisory engagements.
Research and industry leadership in statistical modeling, machine learning, and commercial data infrastructure.
Sole data leader for the North America division (promoted from Data Scientist in August 2026), architecting commercial and logistics data infrastructure. Engineered machine learning demand-forecasting models, automated multi-source data pipelines, and built internal full-stack applications (Databricks, Streamlit) to optimize inventory planning and streamline operational decision-making.
Conducted macroeconomic nowcasting on GDP using Dynamic Factor Models (DFM) in the Mathematical Informatics Lab, evaluating the impacts of applying advanced regularization techniques (including L1 and L2 regularization) on model accuracy and stability.
Selected open-source web applications, machine learning research, and systems software.
An interactive web application for real-time 3D hand pose estimation and gesture tracking. Runs client-side neural network models directly in the browser with hardware acceleration for high-throughput, low-latency inference.
A custom programming language and interpreter engineered from scratch in modern C++. Features an end-to-end compilation pipeline including lexical tokenization, recursive-descent parsing, AST construction, and an evaluation runtime.
Applied gradient-boosted decision trees (XGBoost), deep neural networks, and synthetic oversampling techniques to classify undernutrition status among school-aged children in the Philippines. Presented at the 4th International Conference on Artificial Intelligence and Data Sciences (ICOAIMS 2023) in Kuantan, Malaysia.
Developed high-dimensional Dynamic Factor Models (DFM) for real-time macroeconomic GDP nowcasting at the Nara Institute of Science and Technology (NAIST) in Japan. Evaluated the impact of L1 (Lasso) and L2 (Ridge) regularization on latent factor estimation accuracy and forecast stability.