Evan is a born and raised New Yorker who joined PeakSpan in August 2025 after interning at the firm during the spring of his senior year and the summer following graduation. He graduated from Boston College in May 2025 with a double major in Mathematics and Computer Science, where he combined his academic interests with hands-on work in software development and data driven research.

Evan is driven by building practical, data informed systems and tools that bring rigor and clarity to real world decisions. Outside of the office, he enjoys watching and playing basketball (go Knicks!) and studying emerging trends in machine learning and artificial intelligence.

At this stage of my career, I’m excited to come in each day because I get to learn quickly while building things that matter. I enjoy taking ambiguous, high-leverage problems, usually ones that sit between technology and investment work, and turning them into reliable systems the team can use immediately. The pace of iteration is a big draw. Receiving feedback from the investment team and iterating on the products I’m building happens daily. I’m also motivated by the chance to drive efficiency through automation and to turn our datasets into a real advantage, whether that’s through better visibility, deeper analysis, or automating tedious workflows. Most of all, it’s the people. I am truly excited and honored to collaborate with smart, pragmatic teammates every single day.

PeakSpan is the right place for me to do my best work because the feedback loop is tight and the work ships into real use quickly. On a small team, ownership is clear, the problems are high leverage, and quality matters because you see the impact immediately. The culture is hyper collaborative, so I can work closely with both the technology and investment teams to get context early and iterate on solutions in a thoughtful way. There’s also a lot of trust and autonomy. I feel that if I make a strong case and execute well, I’m empowered to build. That combination allows me to deliver work that’s genuinely useful and feel passionate about doing so.

The period that most shaped how I work today was studying Mathematics and Computer Science at Boston College, especially the stretch when I was juggling proof heavy math courses with building and debugging real software. That combination forced me to adopt a first principles approach: when something is unclear, I break it down into the smallest possible pieces, define terms precisely, and rebuild the solution step by step. In math, that meant learning to be rigorous about assumptions and edge cases; in computer science, it meant translating that rigor into systems that behave predictably under real constraints. That mindset still drives how I operate today, whether I’m diagnosing a bug, designing a workflow, or turning an ambiguous request into a concrete, testable plan.