How to Become an Amazing Fintech Data Scientist in 2022?

Having the qualifications and the right orientation may not take you far unless you are not willing to do some reality check.

The fintech industry is one sector that churns zillions of data every day, which is one of the primary roles of the industry. Data analysis has been its core operation for ages. With advanced technologies, it is only that the way the data is perceived and processed has changed. Fintech is growing in leaps and bounds every year and so is the need for data analysts. Paired with modern technologies such as the Internet of Things, Blockchain, Artificial Intelligence, and machine learning, it is efficiently sailing through the rough winds of innovation and competition. Fintech is essentially a candidate's market. That implies the aspiring Fintech data scientists have the upper hand in choosing a job that fits best their abilities and mental predisposition. Therefore, becoming a Fintech data scientist shouldn't sound like a challenge when numerous opportunities are waiting to be filled. A LinkedIn report, 'Global Skills Gap' suggests that though tech expertise has reached 1.5 million in 2020, the Fintech industry is struggling to find the right fit for the critical roles and data scientists are no exception. Yet, having the qualifications and the right orientation may not take you far unless you are not willing to do some reality check.

How financial data scientist differs from other data scientists:

Finance being the first sector to put data to use to predict market trends, adapting to data science has only helped it break the silo culture, which for other sectors is once in a time incidence. Given the finance sector's ever-changing landscape and regulatory measures, the simple gathering, analyzing, and making sense of data wouldn't be sufficient. A finance data scientist's job goes beyond these regular functions. From building complex data warehouses to creating algorithms and predicting fraud, a finance data scientist's job can be any combination of these tasks. Their day-to-day functions include:

• Risk management
• Fraud detection
• Customer experience
• Consumer analytics
• Pricing automation
• Algorithmic trading

Here is the caveat – though there is high demand for data scientists in the fintech industry, it currently employs the least percentage of data scientists in the industry. It has directly to do with the skills a finance data scientist should have to deliver the services.

What skill sets a finance data scientist should possess?

In general, a standard data scientist is expected to have business domain knowledge, technological skills, and knowledge in math and statistics. A data scientist's skillsets vary across industries and the difference lies in what extent a particular skill is required for that industry. An analysis by businessoverbroadway.com reveals that 65% of the data scientists in the finance sector are research specialists, while other sectors host data scientists as business managers, developers, creatives, etc. And almost 49% of data scientists in the finance sector identified themselves as business data professionals.

Additionally, as stated in a recent report by fintechfutures.com, there is strong demand for embedded analytics from fintech companies and financial institutions. Clearly, fintech is an evolving sector and is still on its way to finding a system that suits better for its customer need redressal and a platform that can abide by data governance changes. From this analysis, you can figure out areas where your energies need to be focused, and definitely stopping at simple data manipulation techniques is not one.