Why I chose Goldman Sachs
During my freshman year of college, I took a course on radio waves in which my friends and I were inspired to cut up a bunch of key cards to look at the circuits inside of them. That particular experience was a defining moment for me—it was when I realized that I cared a lot about how things worked. I spent the next few years immersed in courses on computer networking, computer architecture, and systems programming. During my (virtual) internship at Goldman Sachs in the summer of 2020, I had the opportunity to start applying and continue learning the material I found interesting in college.
Beyond the technical aspects of the experience, that summer was special because of the people I met. My mentors helped me to think about growing as a professional and encouraged me to reach out to other engineers to learn about various businesses within the firm. My coworkers were supportive throughout the program, helping me learn new tools and understand nuances in the business logic in their codebase.
Throughout the internship, I was excited to wake up in the morning and log on. If this was Goldman Sachs from home, I could not even imagine how much I would enjoy Goldman Sachs in person. Accepting the return offer was an easy decision.
My experience at Goldman Sachs as an analyst
After graduating in 2021, I moved to New York to start as a Core Strats analyst in Systematic Market Making in the Global Markets Division. My team is responsible for writing software that facilitates electronic trading across a variety of different products (e.g., currencies, interest, and commodities like gold).
Electronic trading refers to when market participants buy or sell assets using software rather than over the phone or in person. It is made possible by advances in computing that have been harnessed by engineers at venues like the New York Stock Exchange. To trade electronically, a firm will send an order—a request to buy or sell an asset with parameters like a price, an amount, and instructions on how to execute the trade—to an exchange via their trading software. That exchange runs an algorithm that matches up buy and sell orders by price. The exchange will match the ordered pair with the best price, breaking ties between orders with the same price based on which order arrived first. Due to the way that the exchange and market participants interact, the best price and moment at which to trade can change within a fraction of a second. Trading platforms must be designed to respond to the exchange’s mode of processing and, at times, be highly performant.
In order to solve business problems using technology, my team draws from our knowledge of both computer science and finance. With respect to the first subject, much of the programming and troubleshooting I find myself doing at Goldman Sachs is directly related to what I studied in school. Regarding the second subject, much of the material is new to me, but Goldman Sachs offers its software engineers a number of opportunities to gain exposure to this field. Our team sits on a trading floor, which allows us to absorb financial knowledge in a fun and buzzy environment. Personally, I am learning about finance via explanations from experts at the company and reading and discussing newsletters like Money Stuff with my co-workers.
One of the ways issues in finance and computer science intersect is the need for highly performant code in order to take advantage of market conditions. As discussed earlier, prices can change within a fraction of a second, thus requiring latency-sensitive code to place orders on an exchange. This premium on performance means that our team thinks about computer networks, operating systems, compilers, and synchronization patterns as we develop software. I have read JVM specs, derived time and space complexity expressions, written multi-threaded programs, and refactored data structures in order to write lower latency code.
Similarly, modeling trading workflows requires us to consider a business problem through the lens of computer science. While working on a trading platform, I had the chance to apply my understanding of issues in distributed systems to the lifecycle of a trade. In distributed computing, when multiple processes request computation from another process, the effect of handling messages from one may impact the state (i.e. data) they all share. As it turns out, Wall Street is a distributed system. Trading firms (processes) send orders to an exchange (some other process) that matches up corresponding messages into trades (the computation). Based on the order in which these messages are handled, the market conditions (the state) in which a given command is addressed will be different.
In addition to providing challenging work, Goldman Sachs has an amazing culture. The blend of businesses at the firm means endless chances to learn from professionals an engineer may not encounter otherwise. I once had the opportunity to connect with small business owners as a part of Goldman Sachs’ 10,000 Small Businesses program. Through 10,000 Small Businesses, Goldman Sachs provides access to education, capital and support services to small businesses. I spent time learning about the landscape of small businesses in America and the challenges they face. This was a particularly meaningful experience enhanced by the knowledge of finance and administration that the other Goldman Sachs participants brought to the table. I’ve also made friends through work with whom I’ve had the chance to explore New York City. Most importantly, I love working with my team. Apart from being kind, funny, and extremely knowledgeable, my coworkers are great mentors. They have made me feel emboldened to speak up in meetings, have taught me habits that help me program and plan more efficiently, and constantly encourage me to focus on learning in my first year. Moreover, working at Goldman Sachs means working within global teams. My group comprises individuals in New York, London, Hong Kong, Bangalore, and Tokyo. This means opportunities to meet people from all over and even to travel between locations. In fact, I am wrapping up this essay from the London office!
In short, my time at Goldman Sachs has been full of opportunities to grow. Although my commute to work is a bit different (the E train is now involved), one thing certainly hasn’t changed since my internship—I am still excited to wake up in the morning and log on.
Acknowledgments
This blog post came together thanks to the support of many people. Lizzy Wang, an analyst and RTC representative at Goldman managed this project and coordinated all the necessary reviews. Kelsey Lafer, my mentor since the internship, helped me workshop the style and content of this essay. Mohsen Azarbadegan, my manager, also carefully reviewed my essay and has helped me learn about electronic trading throughout my time at GS. The information about exchanges comes from chats I’ve had with my team as an intern and as an analyst as well as CS349F, a course that I took in college.
Lexi works in the New York office as an engineer in a quantitative strategist role. Quantitative Strategists are at the cutting edge of Goldman Sachs’ business, solving real-world problems through a variety of analytical methods. Working in close collaboration with bankers, traders, and portfolio managers across the firm, their invaluable quantitative perspectives on complex financial and technical challenges power our business decisions. Read more about our engineering roles at https://www.goldmansachs.com/careers/divisions/engineering/.
See https://www.gs.com/disclaimer/global_email for important risk disclosures, conflicts of interest, and other terms and conditions relating to this blog and your reliance on information contained in it.
To read more stories in the Goldman Sachs Blog Series visit https://rewritingthecode.org/goldmansachs/.