Sixteen years across three industries.

Apella builds ambient AI for operating rooms — software that gives surgical teams real-time OR visibility, predictive case-duration forecasts, and automated workflow tooling, helping hospitals serve more patients without adding staff. I work across the ML platform: training, deployment, and the developer tools that hold it all together.

• Led a team of three software engineers designing, developing, and maintaining a suite of in-house data management applications
• Achieved 80%+ reduction in manual data patching requests, boosting customer success team efficiency and reducing human errors
• Identified and addressed technical debt, increasing test coverage to over 80% across the codebase
• Provided comprehensive technical documentation, adopted best practices, and automated workflows to increase team productivity

• Contributed to a next-gen metadata-driven Human Capital Management platform built on highly scalable Node.js microservices
• Built and launched microservices handling metadata compilation, validation, and resolution, serving millions of daily requests
• Contributed to the Couchbase-to-AWS data migration, reducing licensing costs by more than $75,000
• Implemented service-health instrumentation across Kibana, Datadog, and Honeycomb, improving system reliability and reducing debugging time
• Mentored co-ops and ran Lunch & Learn sessions to share knowledge across teams

• First full-time employee — built research infrastructure through the firm's transition from a boutique research startup to a hedge fund managing over $1 billion
• Designed, developed, and maintained the Risk and Order Management System, Value-at-Risk monitor, and real-time market analytics platform
• Compiled real-time market data and built a charting framework (R) supporting research findings and publications

• Implemented mean-reversion and momentum statistical arbitrage strategies across equities, futures, and FX markets (MATLAB/R)
• Performed back-testing analysis with transaction costs, extended execution assumptions with time-varying adaptive buy/sell signals

• Developed Perl scripts to analyze and report the firm’s automated trading system performance