Analytics and machine learning for finance, energy, and healthcare.
MS Data Science, UVA·Open to relocation
01 / About
A short introduction.
I build end-to-end systems — from ingest and validation
through modeling, evaluation, and the stakeholder-facing tools that make
the work usable. I’m comfortable on both ends of the stack: writing SQL
stored procedures one day, fine-tuning language models the next.
Most recently I spent two and a half years as a data scientist at the
Virginia Retirement System, where I shipped NLP pipelines for compliance
reporting, factor scoring models, and analytics consumed daily by the Equity,
Risk, and Treasury teams. Before that I worked in energy markets on
operational data feeding industrial price models, and I recently wrapped a
computer-vision capstone at the UVA School of Medicine on surgical instrument
recognition.
I have an MS in Data Science from the University of Virginia (4.0 GPA) and a
quantitative undergrad in Math and Economics.
02 / Work
Selected experience.
2023 — 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 — 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
Featured work.
Surgical instrument recognition
UVA School of Medicine · Capstone
Trained YOLO11 + OpenCV computer vision models to segment and classify 25+
surgical instruments from operating-room video, cross-referenced against
surgeon preference cards to quantify utilization and identify
waste-reduction opportunities. Delivered to clinical stakeholders at the
PeriOp Green academic lab.
Source materials are not publicly distributed at the lab’s
preference; high-level summary and public-dataset companion
available on request.
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.
I’m actively looking for my next role — full-time data scientist, data analyst, or
ML engineer positions, either remote or on-site (open to relocation).
The fastest way to reach me is email or LinkedIn.