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How to Become a Financial Data Scientist

The financial industry has been one of the early adopters of the field of data science and the need for financial data scientist role has been growing rapidly. Data science, as applied to finance, is the field where you build systems and processes to extract insights from financial data in various forms. The finance professionals have always been doing data science in the form of statistical analysis, forecasting, and risk analysis, among other things, however, we now have a industry recognized term for it (data science!) and formal career options around it.

Financial Data Scientist

As we know, the financial services companies are highly information-driven and stand to gain tremendously from insights from their information to improve their top-line as well as bottom-line. Data science can help banks in almost all areas of work including the following:

  • Risk monitoring
  • Trade surveillance
  • Payments
  • Fraud
  • Claims
  • Fintech
  • Social Media
  • Customer experience
  • And more

For example, a data scientist could be required to build data models for risk analysis or work with credit cards transactions data to identify fraudulent and risky behaviour. In the field of customer service, banks can serve their customers better by analysing their transactional behavioural data using various data science algorithms. Banks can also use data science to forecast various aspects of business such as profitability, delinquency and closure. All financial institutions including JPMorgan, Citibank, Goldman Sachs, HSBC, Deutsche Bank are hiring more data scientists every year and this trend is expected to continue in the coming years.

Skills Required for Financial Data Scientist Role

A financial data scientist or a team of data scientists working together as a team in a company would have skills around these four areas:

1. Data Analysis / Quantitative Techniques

Knowledge required to perform data analysis which would includes statistics, decision sciences, operations research, econometrics and predictive analytics. This I think is the most important piece in the data science puzzle. It is important that the data scientist is able to define the data analysis problem, understand the quality of data, fill the gaps in the data or make the right assumptions about it, select the right statistical models to apply on the data, perform the analysis using the technical tools, correctly infer the results of the analysis, and finally present the results in a meaningful way to the stakeholders. One thing that needs special mentioning here is to learn Time Series Analysis since most of the financial data is time-series data.

Data analysis is a complete field by itself and you can apply it to any domain and on any data. You don’t  need any big tools or programming skills to know how to perform the analysis. But this knowledge is foundational to your financial data scientist career.  

2. Technical Knowledge

Along with the knowledge about the data analysis and the statistical techniques, the second important thing is the knowledge of the tools used to perform the data analysis. This is a hot area and this is how a data scientist gets their job done.

Typically when you have to perform analysis on a data set, it is going to be large,  probably containing hundreds of thousands of records. You can neither do this analysis manually nor would you be able to efficiently do it with MS Excel. Since we’re dealing with large amounts of data, data scientists would use a variety of tools and programming languages to perform their job.

Currently the two most preferred tools of choice for data scientists are Python and R programming. Both are very popular programming languages for statistics and various kinds of data analysis and visualizations.

Both have their own advantages and disadvantages. R was specifically developed with data analysis and statisticians in mind. Python on the other hand, has grown rapidly in popularity and has huge community and package support for data analysis capabilities. I have always suggested that a data scientist should invest in learning both.

Since most of the data is stored in databases, you would also need to learn about databases and how to retrieve data from databases using SQL and NoSQL.

Apart from this, as the financial institutions focus on investing in big data technologies, it would also become important for data scientists to get themselves equipped with dealing with big data. You will gain advantage by learning frameworks such as Hadoop, Mapreduce, Spark and even machine learning, but that’s for advanced users. There are lots of other things you can learn to attain mastery but we will leave it out of scope for this article.

3. Data Munging

Skills required to deal with your data is the difference between an average and great data scientist. Since the data is big you will also need to learn  data munging and data cleaning techniques.  

Data munging, also called data wrangling involves converting raw data to another format that is easier to access and analyse. Sure, you do this using the technology tools available to you, but  it requires a different bend of mind to be able to absorb and form relationships between various data sources and combine them in an accurate and meaningful way. You will use technology, statistical skills as well as your experience to do this. This is probably the most time-consuming job performed by data scientists as the data comes in many forms and from a variety of sources.

4. Domain Knowledge

The fourth piece of the puzzle is to have the domain knowledge of the specific field of data that you are looking to analyse. For example, if the analysis is of loans related data, the data scientist would be expected to understand how loans work, how banks manage loan portfolios, and so on. In the context of a bank, you would benefit from learning financial analysis, economics, risk analysis, portfolio management, and acquiring knowledge about financial markets among other things. While learning financial concepts, pay special attention to the maths behind various concepts and the data needs behind them.

When you combine your knowledge of data analysis and statistical techniques, technology skills to perform the analysis, and the financial domain knowledge, you become a financial data scientist and you are in a position to get the best insights from the financial data.  

 

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