Big data is big money. Data scientists looking for a field to park their skillsets should consider the finance industry. After logistics, fintech is bringing in the highest ROI for its big data investments. This growth is creating a significant demand for data analysts, with new jobs, and even new job descriptions, being created in fintech every day.
Experts such as the financial tech firm Cane Bay Company use data scientists for their skills in both statistics and engineering. Data science for example was able to accurately predict four out of six Grammy Awards winners using Spotify streaming data.
Data scientists in fintech pick the winners too, but their winners are winning money. How do they do it? Learn more about that here.
What is the Earning Potential of a Data Scientist in Fintech?
You might think that a data scientist in fintech is piling on the cash, but that’s not necessarily the case. Like any career, the earning potential can be lucrative, but the starting salary is not as high as you would expect. An analyst may make $45 thousand their first year, while their team head is making six figures. Engineers and hedge fund workers might make a little more.
It also depends where you work. If you work in a banking firm or investment company, your income will be average. Hedge fund scientists are going to make more than that.
The bottom line? Data scientists aren’t doing it to become millionaires. They love the job enough to develop the advanced skill set required to do it. Their job duties include analysis of all kinds.
Perhaps the most common data scientist job in fintech is risk analysis. This is a planning job where analysts look for the risks in the customer base. These jobs don’t simply analyze risk ratios, they also are strategic planners and run reports on their firm and their industry.
These jobs need a strong statistics background, and they need to be good at numbers. They look at numbers in the customer base, competition, and in the market.
Financial fraud today is performed using data, and so data analysts in these departments are busy looking for those bytes that are out of place. Fraud is expected in fintech. It’s there, and it’s their job to find it.
Data scientists here need to develop algorithms to predict, process, and analyze human behavior. They need to know people, and the kinds of fraud they will commit. Then they need to come up with a computer program that will prevent that, or detect it.
Analyzing Customer Behavior
An analyst in the consumer division is watching customer behavior closely. They will look at their in-person behavior, such as how long they spend at the ATM. They will also look at their online behavior, such as how long they spend at the ATM. Actions performed there are monitored, analyzed, and used to create better consumer experiences. This isn’t a fraud detection department, there is another department for that.
This is a department that really wants to understand what their customers want, and how to give it to them. They need to understand humans when they see them on the CCTV. They need to understand humans when they are punching in numbers online.
Become a Data Scientist
If this intrigues you, you may be ready to learn more about humans in the world of money. Learn more about data science in fintech, and become one of the largest growing fields today.