Senior Principal Data Scientist / AI Engineering Leader

I build high-stakes applied-ML and AI systems that are measurable, auditable and production-ready — from fraud and graph analytics to LLM evaluation and developer-reliability tooling. The common thread across everything below: the evaluation is itself an engineering artifact. Baselines, negative results, uncertainty, cost, leakage and failure modes are reported, not hidden.

Role on all four: architect and sole author — problem framing, data and evaluation design, system architecture, implementation, reproducibility, deployment surfaces and documentation.

Findings from my tooling that other projects acted on:

• Solidus — 13 dropped %{…} interpolations restored in pt-BR; PR merged the same day with three core-team approvals.
• Plane — Czech template toasts lost their variables; PR merged in three hours by a co-founder.
• nocodb — my issue on translated/dropped vue-i18n variables led to a maintainer PR that swept all 39 locales.
• Excalidraw — my issue on a {{max}}→{{mix}} typo led a contributor to fix it and add a placeholder-parity test suite to a 130k-star repo.

Fraud-lead research pipeline over 11.4M public PPP loans, validated against real enforcement outcomes from DOJ/SBA-OIG sources.

This project focuses on turning public records into transparent, defensible investigative leads — not accusations.

Highlights:

• Built a local analytical warehouse from public SBA/DOJ data
• Used anomaly detection and ML experiments to rank potentially suspicious loans
• Added positive-unlabeled learning and LightGBM experiments
• Benchmarked signals against known prosecuted cases
• Included bootstrap confidence intervals and clear model limitations
• Built Streamlit views for analyst-style review
• Added LLM-assisted entity resolution, retrieval, and similar-case workflows
• Framed outputs responsibly as statistical leads, not proof of fraud

Stack: Python · DuckDB · pandas · scikit-learn · LightGBM · Streamlit · LLM workflows · graph/retrieval methods

I use GitHub organizations to separate different kinds of work instead of mixing every project into one personal account.

Open-source QA for i18n locale files — a deterministic linter (missing keys, dropped placeholders, invalid ICU, collapsed plurals across JSON/YAML/ARB/PO/XLIFF/Android/Apple) with an optional, separately benchmarked LLM semantic pass. CLI, core library, MCP server, GitHub Action, Docker, pre-commit.

What makes it more than a linter: I run it on real open-source repositories, verify every finding by hand against the source language, and send the fix upstream — then turn every false positive the scan exposes into a regression fixture. Running tally with links: shipi18n.com/oss.

Reliability tooling for AI-assisted software engineering. Flagship: tsfix — library-aware TypeScript error recovery for LLM-generated code, deterministic quick-fixes first, an opt-in LLM repair layer second, measured on a real-world failure benchmark (98.6% single-file, 40.0% multi-file, 81.4% aggregate, under $0.005 per fix) with the weak cases reported alongside the headline.

I am building public portfolio projects that show the kind of work I can discuss openly when client work cannot be shared in detail.

My emphasis is on:

• Messy real-world data
• Transparent assumptions
• Reproducible pipelines
• Honest validation
• Practical analyst workflows
• LLMs used as workflow support, not magic black boxes
• Small tools that solve specific developer pain points

Python · SQL · DuckDB · pandas · scikit-learn · LightGBM · Streamlit · AWS · LLM APIs · LangChain · LangGraph · JavaScript/TypeScript · GitHub Actions

• Fraud detection and program integrity
• Public-sector and government data systems
• Applied ML that survives honest evaluation
• LLMs for retrieval, triage, entity resolution, and analyst workflows
• Reproducible data pipelines
• Developer tools and workflow automation
• API/spec tooling
• Lightweight SaaS products