I turn complex operational, customer, and market data into reliable, decision-ready products. My work connects data quality, analytics engineering, experimentation, and applied AI evaluation.
Each case study shows how I approached the problem, what evidence I produced, and what business decision the work supports.
Selected work
Each case study explains the problem, the system, the finding, and the decision. Tools support the story. They do not replace it.
A controlled analytics and monitoring pipeline for spend, contract, vendor, and approval data.
Conversion funnel, retention, and revenue-driver analysis.
A repeatable monthly reporting process for operational managers.
Measured validation defines where automation must stop.
Regime analysis with explicit bias controls and stability tests.
System thinking
A prototype shows that a system can work. Controls show that users can trust it when conditions change.
How I work
A useful result must be repeatable, tested, and connected to a user decision.
I prefer controlled pipelines to one-time analysis.
I test data and model outputs before I automate decisions.
I select metrics and views from the decision that the user must make.
Concept library
Each concept has a short definition, a visual explanation, an implementation, and proof from a project.
Pipeline reliability
A repeated run produces the same valid result.
Explore concept →Transformation
Governed models move business logic out of the dashboard.
Explore concept →Orchestration
A workflow controls dependencies, failures, and reruns.
Explore concept →