JPMorgan’s Athena platform runs tens of millions of lines of Python. Goldman Sachs has been folding the language deeper into the systems around SecDB. Yet plenty of coverage bankers still treat coding as optional. So is the language actually gaining ground with investment bankers, or just with the people who sit near them?
We first asked whether Python had become the first language of finance here. The sharper question now is how far that claim reaches into classic investment banking.
Python owns quant, risk, trading and data teams inside the banks. Classic coverage work in M&A, ECM and DCM still runs on Excel. More analysts are quietly using it to clean data and kill repetitive tasks, especially now AI tools have lowered the barrier. Is that shift turning into real hiring demand, or is it still optional?
What Do the Numbers Suggest?
Where does Python actually rank when banks post jobs? It sits near the top for finance tech and quant roles. SQL often leads the overall bank listings, with Python and Java close behind. That is the picture painted when eFinancialCareers examined listings across major investment banks.
Look at the institutions themselves. JPMorgan’s Athena has been cited for years as one of the largest Python codebases in finance, with figures ranging from 35 million lines upward. Goldman Sachs has been making its risk and pricing systems more accessible through Python wrappers and its open-source GS Quant toolkit. Many of the largest US banks now treat Python as the default language for new data and machine-learning work.
Yet the hiring picture remains lopsided. An analysis of more than 2,700 finance-related job postings across 42 companies found that investment management firms mentioned Python far more frequently (39%) than banks (13%). Jobs in pure investment banking and private equity categories barely registered at all. Junior CVs, meanwhile, are flooded with the language. Does that supply pressure eventually force the coverage side to catch up?
Why Does Excel Still Own the Coverage Floor?
Have you noticed that pure IB analyst and associate roles in M&A, ECM and DCM still rarely list Python as required? Excel modelling, presentation and process skills remain the non-negotiables.
Bankers themselves will tell you the datasets in coverage work are rarely big enough to force a switch. Complicated code is often discouraged because seniors and clients need to follow the model. Does that mean Python has no place, or just a different place?
Where Python earns its keep is the work Excel starts to choke on. Large data pulls, multi-scenario valuation, Monte Carlo runs, automated reporting, anything that needs to talk to an API. Excel’s immediacy is still unbeatable for quick, transparent client work. There is zero setup time and you can have an answer in a minute. Python wins when the data gets large or the process needs to be repeated cleanly.
AI coding tools are changing the calculation. Quants at the banks call tools such as GitHub Copilot a revelation. They speed coding by multiples and make Python usable for people who never thought of themselves as developers. The old barrier is falling.
What if You Are Hiring?
For pure coverage and M&A seats, does Excel still come first? Yes. Python is a differentiator, not a gatekeeper. Knowing both is increasingly powerful. That is the practical path laid out when the new investment-banker toolkit is examined for 2026.
Once the role touches risk, structuring, derivatives, research or anything data-heavy, the bar is moving from nice-to-have to expected. Banks and funds want people who can move fluidly between Excel for client-facing models and auditability, and Python for scale and automation.
A practical path still looks straightforward. Lock down Excel and financial modelling first. Then add targeted Python. The point is not to rebuild the entire model in code. It is to remove the slow, repetitive steps that currently sit between the data and the slide. Excel stays as the delivery layer that a senior or a client can open and follow; Python becomes the engine underneath.
The hiring implication is clear. For a pure coverage seat, strong Excel and modelling skills remain non-negotiable. Python on the CV is a signal that the candidate can move faster once the data gets messy. For any role that sits closer to risk, structuring or research, the absence of Python is starting to look like a gap.
The cost of not knowing Python keeps falling while the upside of using it keeps rising. The gap between the quant floor and the coverage floor is closing. The only real question left is how fast, and where it actually matters for you.
Author: Vagner Dos Santos Trindade
The editorial team at #DisruptionBanking has taken all precautions to ensure that no persons or organisations have been adversely affected or offered any sort of financial advice in this article. This article is most definitely not financial advice
See Also:
Python: the First Language of Finance?
The Rise of AI in Trading: How the Sell Side Uses It | Disruption Banking
