What will investment banking analyst jobs look like in 2030? Generative AI is already shaking up a wide range of professions and, while some like engineering may not need to be as technical as they once were, young bankers may need to brush up on their computing skills.
Deepali Vyas, global head of data, AI and fintech at recruitment firm Kornferry, says that "anybody looking to go into these industries, if you're a traditional major, should bolt on some data and analytics skills." In a world where the more traditional tasks can be done at the press of a button, showing you have "great math skills and great fundamentals is going to differentiate you."
Some investment banking interns are already aboard this train. Andrea Lui, a New York-based incoming investment banking intern at BNP Paribas (one of the more exclusive internships in finance) is studying finance but minoring in computing and data science; she says "having the data science edge really helps." The notion among interns on Wall Street these days is that "most of the work is going to be technical", and "might require Python or some other language, not just Excel."
The inverse effect may also be happening for data scientists themselves; industry expertise is becoming much more valuable. Michael Abdul, a fintech recruiter at Volition, says data scientists are becoming more focused on "strategic aspects, the tech side is becoming easier." Abdul says many analytically minded businesspeople transition to data science because "it's easier to transition into it than it would be into software."
If you're unsure what data science jobs entail, or have missed your opportunity to undertake data science and analytics modules, Abdul says it's mostly about manipulating data to "realize business goals." You should be able to effectively quantify how teams, processes and other business aspects can either earn more revenue or save more money. Vyas says you need to effectively "extract insights from both structured and unstructured data." The three most important programming languages in the space are SQL, Python and R; you may want to prioritize Python as Abdul says "R isn't as popular in the front office any more."
These data science fundamentals, and python expertise, would also make it much easier to transition careers if banking doesn't turn out to be your raison d'etre. You probably won't cut it in quant research at a hedge fund (those are often more focused on hardcore math), but at startups and fintechs which often require a more generalist profile, might not be too hard to transition into.
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