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.
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.