Directors View

The majority of us will remember the original version of the 1987 movie “Wall Street” where Michael Douglas plays the role of “Gordon Gecko”.

This movie single-handedly convinced a generation of finance graduates to become investment bankers in the 90’s and early 2000’s. Of course, reality turned out differently and the majority of an investment banking analyst’s time was spent preparing slide decks till late at night and updating their financial models over weekends. Trading, M&A and raising capital become the de-facto revenue streams for investment bankers during this period.

After the financial crisis in 2008, banks cut head count and smaller fintech companies started up all over the globe, especially in Silicon Valley. At some point in time, “data” suddenly became cool and eventually Business Schools started offering analytics courses as part of their curriculum.

Fast forward to 2020 and the hottest career is that of data scientist. Of course, compared to investment banking both jobs similarly extract clean data, analyse it and present it in an intelligent format to decision makers.

Data science can be viewed as a generalist term, in that it combines business, coding and statistics. Investment banking analysts generate “investment” ideas which are pitched to clients with the underlying support of financial models. Of course, in their case the data is from paid sources such as Reuters and Bloomberg.

In the case of Data Scientists, data is almost always proprietary and relies heavily on the usage of tools such as Python for visualization, machine learning and analysis.

As data science matures, analytical infrastructure will more and more become available via out-of-the-box SaaS which will further accelerate this trend. Of course, technically-minded graduates will opt for software engineering whilst the MBA-types will address the short fall in data science itself.

A quick look on some of the executive placement agencies, such as Glassdoor, in the USA shows that the average salary of an investment banking analyst for JP Morgan or Goldman Sachs ranges around USD 87 000 per annum, whilst the salaries on offer from Microsoft, Google and Facebook for data scientists are hovering around the USD 120 000 per annum mark.

The rationale for the discrepancy in salaries of course is the old adage of supply and demand. There are many more investment bankers available in the jobs pool worldwide than data scientists from a scarcity perspective.

Another interesting similarity between investment banking analysts and data scientists is that professions are geared for people in their late twenties or early thirties, preferably without families, owing to the excessive time requirements on a daily basis. Hobbies and children definitely a no-no. As a matter of fact, it is well known on Wall Street that you can’t aspire to come an investment analyst if you are over 30 and that it is not uncommon for some of them to have sleeping bags under their desks in the office.

Owing to the fact that both professions are effectively number-crunchers, their skills effectiveness plateaus at an early stage. If the individual does not scale up into an executive role managing other people or projects, both end up as dead end jobs.

This can be contrasted to software development which suffers to a lesser degree from ageism because employers can fast track an individual, without management skill, into an architect role.

The big tech companies are slowly becoming the new banks in society. Facebook has Libra, Apple has rolled out Apple Pay and Tencent in China is already used by over 80% of merchants thru an app called WeChatPay.

In conclusion a company that owns our browsing data and procurement history will most certainly end up cannibalising the banks in the future. Don’t be surprised to see that the intersection of AI and finance will lead to an ever increasing number of ex-bankers moving into IT in the nearby future.

Everybody wants to be a data scientist. Something to ponder.

Until next time, stay safe.

Johan de Villiers


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