I am a mathematically grounded senior ML systems and algorithms practitioner specializing in interpretable statistical learning, graph methods, and enterprise-scale analytical systems.
I help enterprise engineering teams navigate the complex transition from machine learning prototypes to stable, production-grade business applications.
With a background combining machine learning research (ML PhD) and practical software design, I work alongside technical leaders to mitigate execution risks, structural bottlenecks, and the engineering debt that frequently stall corporate AI initiatives.
To streamline the development lifecycle, my consulting framework integrates open-source engineering assets designed to bring structure to data science workflows:
- Data Science Knowledge Governance (
KMDS): An ontology-backed workflow engine that captures experimental rationale, organizes engineering context, and preserves corporate IP during team transitions. - Rigorous Engineering Exploration (
tseda): Analytical methodologies that automate early-stage data diagnostics, shortening the validation cycle for complex business use cases.