I've spent 6 years inside fraud — detecting patterns, investigating financial crime, protecting revenue at scale. Now I'm building the technical foundation to work on the other side of that problem: the pipelines and systems that make detection actually work.
I'm a fraud specialist with 6 years of experience detecting complex fraud patterns, investigating financial crime, and protecting revenue — including at Apple. I'm not a generalist. I know fraud data from the inside: the edge cases, the false positives, the messy pipelines that produce bad signals.
I'm now combining that domain expertise with data engineering skills — SQL, analytics, and pipeline thinking — to offer something most data professionals can't: I understand what the data actually means, not just how to move it.
My philosophy is simple: garbage in, garbage out. The best fraud models fail on bad data. I'm here to build the infrastructure that makes them work.
// Projects are being built publicly. Follow along on GitHub.
Open to freelance projects in fraud analytics, data work, and risk. Also happy to connect with anyone on a similar path.