How to become a financial Data Scientist?

NOV 03, 2021

It is quite impossible to think of a field that doesn’t rely on data science. Out of all the industries, finance sector seems to be the one where we can find heavy usage of data science. Being the backbone of the world’s economy, the financial industry has long back understood the importance of data for making informed profitable decisions. In the finance industry, the need to transform data to detect frauds, to establish how the stock market works and most importantly to improve the experience of the customers has always seen an upward trajectory. This is exactly why a financial data scientist is always high in demand.

The major areas that a financial data scientist looks into include fraud detection, consumer analytics, risk management and customer experience among others. If you have been aspiring all this while to become a data science expert in the field of finance but are not sure as to how to proceed then you are at the right place. In this article, we will talk about how to become a financial data scientist and what are the pre-requisites of the same. Keep reading!

How to become a financial data scientist?

Since the very core of a financial data scientist is a blend of finance and data, you are expected to possess certain skills that showcase the same. Usually, skills in the below mentioned areas are desired to land up a role as a financial data scientist –

Data analysis

Since the job role demands the professional to work on data, it is quite evident that he/she is familiar with data analysis and its techniques. Here, everything from statistics, decision sciences, operations research, and econometrics to predictive analytics is taken into account. A financial data scientist should not only be able to define the data analysis problem but also judge how good the quality of data is, make the right assumptions wherever required, make use of the right statistical models to work on the data, perform data analysis using the required technical tools, infer the results of the analysis correctly and lastly present the data in a meaningful format to the stakeholders. Simply put, sound knowledge of data analysis is the key for a successful financial data scientist.

Should be technically sound

It is quite obvious that the data you’d be dealing with would be humungous. Thus, the manual analysis wouldn’t serve any purpose. It is here that technical tools come into play. It is important to realise that in addition to data analysis, one must be able to use a set of tools and programming languages to excel at what they are doing. On that note, Python, R, SQL, NoSQL, etc. are the most common tools/languages that come in handy for a financial data scientist. Also, as there is no limit to the amount of knowledge one wishes to gain, you can always go a step further to try your hands on frameworks such as Hadoop, Mapreduce, Spark and machine learning.

Data wrangling

Data wrangling (the process of converting raw data into a meaningful form) is one of the most crucial tasks of data science. Yes, tools and technologies do help here. But a mind that is able to absorb and form relationships between various data sources and combine them efficiently in an accurate and meaningful way makes it a lot easier.

Knowledge of key systems used in the finance industry

As a financial data scientist, it is expected that you have significant knowledge of key systems used in the finance industry such as SAP, SWIFT, Oracle, etc.

The above-mentioned skills play a pivotal role when it comes to becoming a successful financial data scientist. You don’t need a degree to justify that you are knowledgeable enough. These skills can be acquired and polished using other mediums as well such as online courses, boot camps, reading books, etc. Ultimately, what everything boils down to is what you bring to the table and what role do you play in enabling the business to achieve its goals.

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