Joined a climate tech startup to apply my technical skills toward meaningful environmental impact. Developed an AI-powered chatbot using cutting-edge RAG technology that transformed how architects access building codes—what once required hours of manual research became instant conversational queries. I also built an automation system that revolutionized our compliance workflow, developing unique algorithms and tools that reduced COMcheck reporting time from 80 hours to 20 hours for our architectural researchers and consultants. Additionally tackled performance challenges in our 3D modeling platform, optimizing rendering speed by 26% through code refactoring and algorithmic improvements. This role deepened my passion for using data and AI to solve real-world problems at scale.

During the pandemic, I joined a remote startup building tools to democratize software development. The vision resonated with me: enabling non-technical users to build database-driven applications without writing code. I developed the core platform components that translated intuitive user actions into SQL schemas and data workflows, essentially building a bridge between visual interfaces and complex database operations. Working remotely across time zones taught me to communicate technical decisions clearly and collaborate effectively with distributed teams.

After my success in cryptocurrency trading, I wanted to test my quantitative approach in traditional markets on Wall Street. At T3, I traded equities, commodities, and currencies using systematic strategies, building financial models and conducting in-depth technology sector research. The experience was invaluable—I refined my ability to analyze complex market data, make decisions under pressure, and adapt strategies in real-time. While I gained deep expertise in quantitative trading, I ultimately found myself drawn to building the analytical tools and systems themselves, which led me to pursue software engineering.

Fresh out of my finance Master's program, I became fascinated by cryptocurrencies—not just as investments, but as a technological revolution. I treated this as a quantitative research project, applying time-series analysis, technical indicators, and risk management frameworks to what was essentially a massive, volatile dataset. Starting with $25,000, I grew the portfolio to $675,000 by staying disciplined and data-driven even as the market swung wildly. But the real transformation wasn't financial—it was realizing how much I loved working with data and solving analytical puzzles. This experience sparked my curiosity about blockchain technology and ultimately motivated my transition into software engineering.

During my Master's in Finance at Northeastern, I joined the student-run 360 Huntington Fund—a graduate investment fund where MBA and MS Finance students manage a real portfolio. I covered four equity positions, building valuation models and analyzing earnings to deliver hold/sell recommendations to the fund managers. The most challenging moments came when I had to make contrarian recommendations: defending Lululemon during a 20%+ drawdown and arguing to hold Google when it reached its target price. Both calls required synthesizing quantitative models with qualitative judgment, and both ultimately generated 30%+ gains. I also analyzed the fund's overall risk exposure using factor models. This hands-on experience taught me that strong analysis requires both rigorous quantitative methods and the confidence to act on your conclusions.