Quantitative Researcher | Data Scientist | AI/ML Engineer
15+ years applying machine learning and AI techniques to quantitative finance, building predictive models across global equity markets, and developing high-performance tools for data science workflows.
I'm a Quantitative Researcher with expertise in designing, prototyping, and deploying predictive models across global equity universes. My work spans quantitative finance, fraud detection, alpha/risk modeling, and building scalable ML/AI systems.
Most recently, as Head of Quantitative Research at Victory Capital (Sophus Capital), I maintained the Global EM alpha factor model. I've also led data science teams at Macquarie Asset Management and CO-OP Financial Services, driving innovation in predictive modeling and ML deployment.
I hold graduate degrees from NYU (MS Financial Engineering, MS Computer Science), University of Chicago Booth (MBA), and Iowa State University (MS Statistics).
Builder of production AI systems including RAG pipelines, vector search engines, and audio/document analysis servers deployed on GPU-accelerated infrastructure.
ChromaDB-powered vector database for semantic search across 17+ source types. Full RAG pipeline with sentence-transformer embeddings, multi-signal credibility scoring, staleness-aware retrieval, and automated batch ingestion with intelligent routing across 12 extractors. Exposed as a REST API (FastAPI) and MCP server.
MCP server for PDF processing powered by Claude API. Multi-page iterative extraction, document classification across 17+ content types, OCR support via Tesseract, and hash-based caching to prevent re-processing.
Production MCP server for audio analysis. Whisper transcription with word-level timestamps, pyannote.audio speaker diarization, prosody analysis, and sentiment detection. GPU-accelerated with low-VRAM mode for constrained hardware.
CO-OP Financial Services: Real-time fraud prediction for credit card transactions within 50ms for 100 Credit Union clients. Model detected 4-5% more fraud vs industry standard using XGBoost and Random Forest on Azure Databricks with engineered behavioral features.
A comprehensive R package for streamlined ETL operations. Provides tools for data transformation, file management, cloud storage (S3) integration, and feature engineering. Built on dplyr, lubridate, and arrow for efficient data processing pipelines.
An R toolkit for streamlined model development, training, and forecasting at scale. Unified interface for base R and parsnip models (glmnet, mgcv, rstanarm), with support for rolling/expanding windows, walk-forward validation, and tidy exports to partitioned datasets.
Principal Global Investors: Predictive volatility trading signal for S&P 500 used in variable annuity hedging strategy. Delivered 21% better risk-adjusted returns (Sharpe Ratio) from 2012-2016 vs peers by dynamically allocating between equity and cash targeting 15% max annual vol.
I'm always interested in discussing quantitative research, data science opportunities, and collaborating on innovative projects. Feel free to reach out!