Staff Data Scientist at Abodemine
I build production data science systems with a current focus on AI-assisted data integration, schema matching, machine learning, analytics platforms, and decision products.
Experience
My work sits at the intersection of modeling, software engineering, and product judgment: building tools that are accurate, observable, and useful to the people who depend on them.
Professional Work
Currently Staff Data Scientist at Abodemine, where my work includes matching client workbook schemas to an internal canonical schema with AI and LLM-assisted workflows. Previously, I grew from Data Analyst to Senior Data Scientist at Prove, where I worked on trust, identity, model monitoring, international scoring, and production analytics workflows.
Read more about my experienceCurrent AI Work
At Abodemine, I work on AI-assisted schema matching: mapping client workbook fields into an internal canonical schema so heterogeneous data can move through consistent downstream systems.
Problem Space
Client workbooks rarely arrive with clean, predictable field names. I help design systems that infer intent from column names, workbook context, examples, and business rules.
AI Application
The work combines prompt design, structured outputs, validation logic, and reviewable confidence signals so AI suggestions can fit into production data workflows.
Why It Matters
This is the kind of AI work that matters in industry: reducing manual mapping burden, improving consistency, and making complex ingestion workflows easier to scale.
Older personal projects are still available in the project archive.
Research Foundation
I earned my PhD at UC Santa Barbara studying white dwarf binaries with theoretical and computational models. That training still shapes how I approach uncertainty, simulation, validation, and technical communication.
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