Is there a financial data scientist drought?

Financial organizations will depend on financial data scientists to design, create, and maintain algorithms, but there's a lack of training and education offered in order to fulfill this role

Finance analyzing team    180443855
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Big data has infiltrated all industries, from medical to governmental, creating a skills gap across the globe. Coined "The Sexiest Job of 2016" by Harvard Business Review, data science went from barely existing 20 years ago to being highly in demand today.

Companies big and small are looking to hire data scientists to harness the power of the 2.5 quintillion bytes of data created daily. As the push for more data scientists revs up, organizations are also going to look highly on niche data scientists, a person that specializes in data science and has expertise in another industry.

One interesting niche that has begun to develop in this field is financial data science. This highly unique position combines traditional data science, advanced statistical analysis, and extensive knowledge of financial markets. Since finding one person experienced in all three of these areas is rare, usually a team of experts combined makes up one financial data scientist. But that's beginning to change.

The demand for financial data scientists

In the financial industry, we're seeing banks use big data for improved customer intelligence and risk management. Similar to what happened with the implementation of cloud technologies, businesses must adopt strategies implementing big data quickly and safely in order to stay relevant.

Not only are financial institutions beginning to use big data for marketing, advertising, and better targeting of customers, but they are also implementing big data technologies with advanced machine learning for financial risk assessment and financial modeling.

For example, robo-advisor algorithms are already in place to manage the day-to-day for many bank clients' funds, and it may be only a matter of time before these bots can also interact with clients verbally. However, the demand for more automated, big data solutions doesn't necessarily mean bad news for those currently working in the financial industry. In fact, it has created a major opportunity for a new generation of financial workers, but it will require a new level of analytical skills.

The financial organizations of the future will depend on financial data scientists to design, create, and maintain the algorithms that are taking over the financial world. According to Matthew Dixon, assistant professor of finance at the Illinois Institute of Technology, in an interview for Bloomberg, "The days you could just learn Excel and do some fundamental analysis are over. You're going to be working with larger and larger amounts of data, and you'll need to know how to use algorithms."

So where are the financial data scientists?

For those who think they have what it takes to become a financial data scientist, it will be important to form a fundamental understanding of data science and combine that with advanced analytics and experience with financial markets.

The issue right now is the lack of training and education offered in order to fulfill this role. Almost every major company -- from Google to Facebook to Intel to Amazon -- is looking for a data scientist, and with a high base pay, this position is well sought-after. But a recent Deloitte trends report reiterates the talent shortage, revealing the following statistics:

  • 40% of respondents to a 2015 MIT Sloan Management Review survey say they have difficulty hiring analytical talent.
  • Only 17% of "analytically challenged" firms say they have the talent they need.
  • Among companies reported to be "analytics innovators", 74% said they had the analytics talent needed.

Those looking to gain the title of Financial Data Scientist lack sufficient educational opportunities. Those who want this position are typically combining studies or experience in data science with studies or experience in finance in order to capture the necessary requirements to be hired. Since colleges and universities have been slower to offer this as a course of study, we have also seen a rise in the DIY data scientist.

But what can organizations do to address the demand? To start, companies may consider working closely with universities and programs that have demonstrated their commitment to helping finance students develop data science skills. We may also see a rise in specialized recruiters that will go on-campus, present in classes, and reach out to career centers as the demand for niche data scientists grows.

One such example is the recent partnership between Accenture and the University of California, Berkeley. The organization was involved in the creation of a Big Data and Data Science curriculum, opening new courses and a lecture series to equip MBA students with data analytics skills.

Alternatively, we could see a rise in organizations developing this highly-sought after role from within. This may be in the form of offering formal training programs or reimbursing employees who are attending courses and industry events on their own. For now, while formal educational institutes play catch up, DIY options such as MOOCs and bootcamps are some of the best ways to fuel highly-skilled employees to tap the enormous potential big data presents.

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