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To alumnus Gal Weitz (EngrPhys, ApMath’22), Boulder was a “dream destination” for undergrad. Now working in quantitative finance, Weitz shares how his education at �鶹��Ѱ�����Boulder set him up for success in the finance world.

Finding his path

After spending several years in the military, Weitz had to decide what to study. “I remembered that the only two subjects I truly enjoyed in high school were physics and math. That was my sign,” he said.

An avid cyclist who competed for the Israel National Cycling Team, Boulder provided the perfect setting for Weitz to complete his undergraduate studies –– a “world-class” physics department and a bike-friendly environment.

Weitz was at �鶹��Ѱ�����during the COVID pandemic, which disrupted the traditional college experience with a sudden shift to virtual learning. For many students it was a difficult transition.

“I felt so lucky to be a physics student,” Weitz said. “Our professors were so dedicated –– hand-writing perfect notes on iPads during lectures instead of using pre-written PDFs or PowerPoints. It felt just like a normal lecture with a good old blackboard, minus the breaks to mop the boards which were so satisfying to watch.”

Discovering research

At CU, Weitz explored several research areas before landing on quantum computing.

He conducted research in condensed matter physics with Professor Dan Dessau where he worked on experimental hardware for superconducting materials. He then shifted to experimental quantum information with the Kaufman Group before settling into a focus on quantum computing with Professor Joshua Combes.

In the Combes group, Weitz developed a novel probabilistic algorithm which achieved superior performance over conventional benchmarking techniques in quantum optimization algorithms.

Weitz earned summa cum laude for his undergraduate honors thesis titled “A Classical Performance Benchmarking Scheme for the Quantum Approximate Optimization Algorithm.”

His work from that research group was , with Weitz as first author.

Pivoting to finance

Until late in his junior year, Weitz was set on pursuing a PhD in physics. That is until he discovered “the dark side” of quantitative finance.

“Like many math and physics majors, I was drawn to solving hard quantitative problems,” said Weitz. But at that point it was too late in the recruiting cycle to land a full-time role after graduation.

Weitz pivoted. After graduating in 2022, he worked as an NLP Software Engineer at Magnifi, a tech company in Boulder, while applying to graduate schools.

He went on to complete a master’s degree in financial engineering at Baruch College. He completed an internship at AQR Capital Management, a global investment management firm, where he now works as a Portfolio Implementation and Research Analyst.

A day-to-day in quantitative finance

Weitz says he spends about 50% of his time on portfolio management, 45% on coding, and 5% in meetings.

“On the portfolio management side, I help rebalance portfolios through a quantitative optimization process — aligning them with our signals, risk models, and constraints — and send the trades to the execution team. On the coding side, I work on projects to improve our systems or implement new capabilities that help us analyze how portfolios and signals behave in real-world conditions. This often involves applying math and economics concepts to develop analytical tools. I also collaborate closely with researchers to implement new strategies and with software engineers to integrate our code into production systems.”

Advice for aspiring quants

For anyone looking to get into quantitative finance, Weitz recommends taking electives in applied math and statistics. He adds, “concepts from Markov Chains, Applied Regression, and Mathematical Statistics show up constantly in interviews and on the job.”

Weitz says the interview process usually involves multiple rounds starting with coding and brainteasers.

“Start practicing on LeetCode as early as freshman year, buy the “green book” and learn it inside and out, take the introductory C++ courses offered at CU, and avoid relying on ChatGPT right away. Try to solve problems yourself first, then use it to check and learn from your mistakes.”

Weitz adds having a few technical projects you can discuss in detail, and a computationally focused internship or research experience as a strong resume booster.

Biggest life lesson & final advice

Weitz said “it’s natural to focus on what’s next — the next role, the next milestone — but it’s just as important to pause and appreciate where you are now and what you’ve accomplished. Your past efforts built the person you are today. And above all, make time for family and friends.”

When asked about advice for current or future students, he emphasized that difficult things will inevitably come. “Develop a tendency to embrace them and push through them,” he said. “Like my old unit’s motto, ‘who dares wins’ – dare yourself to take on challenges, and you’ll develop a lifelong winner’s mindset.”

To students at CU, Weitz says “you are in a great place. I would go back in time to sit in your seats in a heartbeat. Sko Buffs!”