Identifying Skillsets of a Data Scientist in the Financial Industry

The wave of big data has taken the world by storm. So much so that it is creating jobs that, 20 years ago, we would never have even heard of. One of those jobs is the job of a data scientist. They existed 20 years ago, but we did not understand what they did, and the demand was low. Learn more about getting into this field right here.

Why do We Need Data Scientists in Finance?

Today, the World Economic Forum estimates that the daily global data creation is 44 zettabytes, and is estimated to reach 563 exabytes in 2025.

One zettabyte is the equivalent of one thousand exabytes, one billion terabytes, or one trillion gigabytes. 44 zettabytes of data are generated daily by the world. We need more data scientists to analyze that. It’s just one of the trillions of reasons that financial tech firms like Cane Bay Partners are saying that data is changing the world.

The world of finance today is data-driven. From our debit card PINs to our portfolio passwords, we can’t access a thing without data. In finance, data is not just used to access finance, but it is also used to develop it. Insights are taken from every byte of data that the finance industry generates daily, and they are generated by data scientists.

If this field interests you, begin the training now.

What Skills are Required of Data Scientists?

If you like data, and you like science, you will make a good data scientist. A data scientist looks at bytes all day long and analyzes them using experimental methods. The ability to analyze data using both quantitative and qualitative techniques is the best skill you will have.

Some scientists in a lab can’t do that They are either quantitative or qualitative, not both. This means that you can crunch numbers (quantitative), or even data that are letters and shapes, and you can also form an analysis on what they mean (qualitative.)

Some of this can not be trained. You need to have the scientific mindset to look at the output of data, interpret it, and form some insights on that data. Then, you will either report that data with your analysis or take your work one leg further by offering insights and opinions on what to do with that data.

You also need technical knowledge, but this can be trained.

Domain Knowledgebase

In addition to technical knowledge, you need domain knowledge. A data scientist in fintech needs to understand where they work. An analyst with the stock exchange will examine who is trading what and when. An analyst in real estate investing will examine the same thing, but they will be looking at industry-specific terminology.

This can also be trained.

Data Management

Data management in this field is also known as data munging. This is converting data from one form to another. You will need to learn how to take raw data and turn it into something that you can analyze. This is like having a cipher to information that nobody else has.

To grow in this skillset, you will need tech, some statistician skills, and your tech experience. You also need to love the finance industry, because you will be getting to know it at its most fundamental level before almost anyone else does.

Learn Data Science in Finance Today

If you love data, love science, and love finance, you will have the skillset you need to excel in the field of fintech. Begin researching the steps you need to take for this career today.

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