Investment banking

Data science has grown by leaps and bounds in the last few years. This growth is similar to the spike in demand for investment bankers, before the 2008 financial crisis. Both roles – data scientists and investment bankers—have a lot more common than this. For instance, both are generalist roles. 

 The following part of the article highlights commonalities between data science and investment bankers. 

Data science and investment banking 

 Data science is a multidisciplinary role that requires knowledge of coding, statistics, and business. Data scientists solve problems by creating machine learning models and then presenting the result to decision-makers. 

Similarly, investment bankers find investment opportunities and support their search and ideas by financial models and pitching their ideas to clients. 

Data scientists use propitiatory data. Python, SQL, Pandas, Numpy, SciKit, Matplotlib, and ML are some frequently used tools in data science. Additionally, data science requires a good amount of computer science and engineering. 

The latter –investment bankers – rely on paid tools like Bloomberg, Reuters, and Capital IQ. Additionally, they use Excel, SAS, and Macros for automation. Investment bankers require knowledge of financial engineering. 

 Business grads are moving to data science. 

Before the 2008- financial crisis, every business grad wanted to work on Wall Street. Now the dynamics have changed and graduates want to work in data science. A large number of seasoned bankers have moved to analytics roles at start-ups. 

 As data became crucial for businesses, global banks started cutting headcounts, and now there are several fin-tech around the globe. Looking at this, business schools have introduced analytics as part of the curriculum.  

 Business grads who would have opted for investment banking certifications to accelerate their pace to get into a global bank are now vying for data science roles at tech giants or fast-growing start-ups. 

Data science is becoming a business role.  

 Data science includes various roles that involve working with data. NLP engineer, ML engineer, data analyst are a few examples. However, if you exclude roles data engineering, ML engineering, software engineering, what remains looks like a business or analytics role. 

 Formulating problems, interpreting data, visualizing patterns, and presentation of analysis appeals to non-coding stakeholders. This is similar to what investment bankers do. 

As data science progresses and industry has more robust analytics infrastructure, demand for data scientists will accelerate. The technically well-versed people may opt for software engineering, while business graduates may go for the role of data scientists. 

 Both offer lucrative salaries. 

Data scientists and investment bankers are among the highest paid jobs around the world. The average salary of a data scientist is $113k, while an investment banker makes an average of $79,847. 

Both culminate in a management role.  

The average age for both roles is nearly 30. You can not become an investment banker if you are over 30. 

Both roles are hectic and might not give you the time for leisure activities. An investment banker goes home nearly at 2 AM. Similarly, a data scientist can be found pulling an all-nighter trying to solve a particular problem. Beyond a certain age, these roles can become too hectic. A 25-year-old without hobbies might be comfortable making corrections in a pitch book, but someone with a family, won’t stay all night making changes to a model. 

Both roles require extensive “number crunching”. Knowledge of the field is mandatory to excel in this field, but effectiveness comes at a standstill after a certain point. The point is – both roles become saturating at one point. 

 Both data scientists and investment bankers are required to move into a managerial role to move ahead in their careers. Thus, they need management skills – people, projects, delegating, sales, and more. This is in contrast with software development roles, where individuals progress to architect and non-managerial roles. 

Technology companies are the new banks.  

Technology companies are beginning to become banks for people. Apple Pay, Google Pay, and more are becoming banks for the current generation. These companies now own financial infrastructure, which was not possible earlier. 

This phenomenon is growing across the globe. For instance, 80% of small SMBs use We Chat Pay, owned by Tencent, in China. The company has grown consistently in the region. North America is currently lagging, but soon the demand for data scientists will rise among the fin-tech companies here. 

 Data is power. Any firm with the knowledge of users knows this already. Today companies that are collecting user and customer data have immense potential, which gives companies a competitive edge over companies that are yet to get on the data science bandwagon. In the long run, this wouldn’t be any different than banks. 

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