Petroleum engineer → data scientist → AI strategist
AI is only as good as the data and governance behind it.
I specialize in Data Governance and AI Strategy — building the frameworks, systems, and teams that make AI reliable and trustworthy at enterprise scale.
Currently a Product Owner for SE Intel, Schneider Electric's global analytics platform, after leading the company's Global Data Quality function and its Data Governance data science team. I also teach Data Strategy, Governance, and Ethical AI as an Adjunct Professor at the Asian Institute of Management.
A Petroleum Engineer turned Data Scientist, my focus is translating complex data and AI challenges into governed data products that the business can actually use and trust.
Experience
Product Owner, SE Intel
@ Schneider ElectricMay 2026 - Present
Owns the product roadmap for SE Intel, Schneider Electric's global analytics platform built to give business leaders the trusted data they need to make strategic decisions.
Adjunct Professor
@ Asian Institute of ManagementApril 2026 - Present
Teaches Data Strategy, Governance, and Ethical AI, helping the next generation of data and AI practitioners build responsible, governance-led frameworks.
Global Data Quality Leader
@ Schneider ElectricMarch 2024 - May 2026
Built and led an 11-person global team of data engineers, data scientists, and analysts; spearheaded end-to-end data quality management for Schneider's global analytics platform.
Co-Founder and CEO
@ Ridge AIAugust 2023 - December 2024
An affordable, plug-and-play AI platform empowering restaurants to leverage advanced analytics for operational efficiency and profitability.
Projects
Pull a card. Each one is a project from my data science notebook.
Cassava Leaf Disease Detection using Neural Networks
Deep learning model using transfer learning on EfficientNetB3 to classify cassava leaf diseases, reaching 89.90% accuracy.
Detection of Animal Species in Tropical Landscape
Audio classification of rainforest species using mel spectrograms, ResNet-50, and AWS-scale data processing.
A Machine Learning Approach to Portfolio Management
A Random Forest stock-picking model that returned +240.70% in a simulated 2016-2020 portfolio, versus a 12.29% PSEi loss.
Creating Monet-style Art using CycleGAN
Using CycleGAN to translate landscape photos into Monet-style paintings without paired training samples.
Writing
A thought dump on data governance, AI strategy, and building trustworthy systems.