Analytics and machine learning for finance, energy, and healthcare.
MS Data Science, UVA·Open to relocation
01 / About
About me.
Recently finished an MS in Data Science at UVA (4.0 GPA), after two
and a half years at the Virginia Retirement System and a quantitative
undergrad in math and economics, also at UVA.
Most of the work has been analytics. Dashboards, scoring models,
document automation, ETL. Some applied ML where it fit, including a
recent computer vision capstone at UVA's medical school. The contract
before VRS was in energy markets, working on generator data for
industrial price models.
Outside of work, mostly cars, cameras, friends, among other things.
I waste money on a 50 year old sports car and Polaroid film, and occasionally a 24 Hours of Lemons team.
02 / Work
Where I worked.
2023 to 2025
Data Scientist
Virginia Retirement System · Richmond, VA
Applied NLP and LLM-based sentiment analysis to 10-K/Q filings, news, and FOMC communications, producing macro signals consumed by the investment team for quarterly outlook reporting.
Built an OCR + NLP document-processing pipeline for SEC ADV compliance, cutting quarterly manual review by ~50% and improving audit traceability.
Engineered SQL stored procedures and views supporting Treasury and Risk reporting.
Led cross-team process redesign and automation initiatives across Real Assets, Credit Strategies, Risk, Treasury, and external vendors (Snowflake, BarraOne), reducing end-to-end processing time by 30%+.
Partnered with Equity, Risk, and Treasury stakeholders to ship Tableau, Streamlit, and Excel/VBA tools, including a cash-visibility dashboard that surfaced previously unrecognized free cash for the Treasury team.
Developed and maintained Python- and MATLAB-based factor scoring models and automated ETL pipelines, including exploratory data analysis on structured and document-based data such as PDF and XML, improving data integrity and reducing manual upkeep.
2022 (contract)
Data Analyst
Tabors Caramanis Rudkevich · Energy markets consultancy
Processed 5M+ records of generator production data to derive operational parameters (min output, min up/down times, ramp constraints) feeding industrial price models and trading simulations.
Built scraping and ingestion pipelines across 10+ facilities, adding 200k+ historical data points and correcting previously misreported entries.
Developed anomaly detection logic to automatically flag forced outages and equipment downtime, reducing manual review.
2018 to 2022
Earlier analytics work
Various organizations · Part-time & project-based
Time-series forecasting (ARIMA), anomaly detection, and visualization on operational datasets including municipal waste collection and athletic-performance data, supporting investigative analysis and policy evaluation.
03 / Projects
Project work.
Surgical instrument recognition
UVA School of Medicine · Capstone
Computer vision models (YOLO11, OpenCV) trained for segmentation and
identification of over two dozen surgical cart instruments and
equipment from hours of video, cross-referenced against surgeon
preference cards to quantify utilization and identify waste-reduction
opportunities. Capstone for the PeriOp Green academic lab at the UVA
School of Medicine.
Source materials are not publicly distributed at the lab’s
preference. High-level summary available on request.
YOLO11 instrument detection. Capstone demo.
LLM fine-tuning, published on Hugging Face
Personal · Open-source
Fine-tuned an open-weights 8B base model with PEFT/AdaLoRA for a headline classification. Published model card, evaluation methodology, and results on Hugging Face
alongside reproducible training code.