What will investment banking analyst roles look like by 2030?
With generative AI shaking up everything from legal to finance, young bankers may need to start thinking less like traditional analysts and more like technical operators. As automation streamlines the basics, skills in data and coding are fast becoming the real differentiator.
Why Data Skills Are Becoming Essential
Deepali Vyas, Global Head of Data, AI, and FinTech at Korn Ferry, says future candidates will need to blend domain knowledge with technical capability:
“If you’re a traditional major, bolt on some data and analytics skills. Strong math fundamentals will set you apart.”
And students are listening. Andrea Lui, incoming investment banking intern at BNP Paribas, is pairing a finance major with a data science minor. She says,
“Having the data science edge really helps — most of the work now is technical, and might require Python, not just Excel.”
Data Scientists Moving Closer to the Business
This shift isn’t just affecting bankers. It’s also changing what it means to be a data scientist.
Michael Abdul, fintech recruiter at Volition, says:
“Data scientists are leaning into strategy more. The tech side is getting easier — and many analytical businesspeople are now pivoting into data roles.”
It’s a space where the core challenge is understanding how to use data to drive outcomes. Abdul adds,
“You need to show how you’re using data to move the needle — to generate revenue, or reduce costs.”
So, What Should You Learn?
The key skills? Python, SQL, and — to a lesser extent — R. While R is falling out of favour in front-office finance, Python remains in high demand across banking, fintech, and tech.
Even if a data science career isn’t the endgame, having those skills can create a fallback path. You might not land in a quant hedge fund, but with the right mix of business context and technical literacy, roles in FinTech and startups are well within reach.
