If you want a technology job at Goldman Sachs, you’ll have to jump all sorts of hurdles, including the notorious Goldman Hackerrank test (which is either easy or incredibly difficult, depending upon who you talk to).
There’s no real opportunity to sidestep this process, but there is a way of differentiating yourself from all the other Goldman Sachs technology applicants which seems to have gone largely unnoticed: You can now build your own product based on Goldman’s open-source APIs.
Goldman moved some of the code underpinning its Marquee risk and pricing system onto Github last year. As a result, anyone can now access GS Quant, the firm’s Python toolkit for quant developers, and can build their own products using Goldman’s data. In fact, you are encouraged to do so.
The people who have interacted with Goldman’s open source API include Jeffrey Zhang, a computer science student at the University of Waterloo in Toronto. Zhang used Goldman’s Marquee data to develop InvestedED, a Python-based webapp that queries Goldman’s dataset, looks at contextual issues, and recommends the best stocks to invest in. There’s also Can Koz, a Turkish developer who developed a tool called Pocket Analyst, a chatbot on Facebook Messenger and Telegram that uses Goldman’s Marquee API alongside data from Blackrock.
Given that neither Zhang nor Koz work for Goldman Sachs, it might be supposed that building your own product based on Goldman’s APIs won’t immediately land you a job at the firm. However, this seems short-sighted given that the firm has said very clearly that it sees providing APIs as an important part of its future. In addition, co-chief information officer Marco Argenti is a big supporter of the strategy.
Goldman declined to comment on the popularity of its APIs on Github. The firm also runs a separate API hub, GSDeveloper; however, the APIs in that instance are accessible to institutional clients only.
A modified version of this article originally appeared in eFinancialCareers.