The Rise of the Financial Data Scientist

What it takes to become a financial data scientist and why financial institutions are recruiting candidates quickly. 

By Vivian Zhang, founder and CTO of NYC Data Science Academy and adjunct professor at Stony Brook University. She is a data scientist who has been devoted to the analytics industry, the development and the use of data technologies for several years. 

The future of finance relies heavily on data. Perhaps more accurately, it relies on professionals that can work with data. In an EY study that focused on the future of finance, 57% of respondents noted that building skills in predictive and prescriptive analytics is critical for the future. When asked what financial skills are necessary five years from now, 53% noted big data and advanced analytics.

The data science skills gap has hit the financial world, just as it has numerous other industries. However, after the logistics industry, the finance industry expects the highest ROI for investing in big data. Given the need for more big data skills and the results both seen and expected, the sector has been recruiting and hiring data scientists as fast as possible. And with this surge in hiring, new positions have emerged, such as the financial data scientist and model and quantitative validation positions. Financial industry regulations have mandated the need for data scientists such as those who validate and challenge models developed by all of the data science and quantitative teams in the company. Now that these positions are a requirement for financial institutions, the need for data scientists in finance is even greater.

Becoming a financial data scientist

Financial data scientists possess a fundamental understanding of all data science skills along with advanced analytical skills, knowledge of the finance industry and the experience of working with financial markets. This position must be able to work with series data and perform data analysis, which means a solid background in statistics, operations, and predictive analytics. In order to work with large amounts of data, most in this position are skilled in R and Python, as well as SQL and NoSQL to retrieve the data from datasets. Many also have a working knowledge of big data technologies such as Hadoop, MapReduce, and Spark with the more advanced candidates having skills in machine learning. Additionally, financial data scientists have experience in data cleaning, data munging, as well as domain knowledge.

On the financial side, candidates understand financial planning and analysis that helps them determine actionable insights. Experience in testing time series models and forecasting is also crucial here. In my experience, financial data scientists have worked in the financial arena in some capacity and also have soft skills such as strong leadership and management abilities. While not always, this role typically leads a data science or finance team in some capacity, so the ability to offer professional development to more junior team members is important as well.

The need for financial data scientists

This position has the unique ability to use technology to reduce costs and increase profits, which makes them very much in-demand. 

In sales, marketing, and advertising, data science helps to target customers better and reduce churn. These companies are also turning to data for risk monitoring and assessment, as well as fraud prevention, trade surveillance, and financial modeling. All of these areas depend on employees who can create, deploy, and maintain algorithms in the financial space. One position that tends to overlap with the financial data scientist is the financial engineer.

The financial engineer

Financial engineering, sometimes referred to as computational finance or mathematical finance, is a position that requires similar skills to the financial data scientist but functions very differently. This person applies financial knowledge along with strong programming and mathematical skills to improve or create new financial products. A financial engineer often acts as an intermediary between the business team and the IT team in a finance company.

The financial engineer typically helps create financial products that can, for instance, assess risk. The financial data scientist is expected to have almost all of the same skills as a financial engineer and additionally applies machine learning techniques to automate data-driven decision-making. Both play a role in building financial models, but the data scientist extracts value from these models.

The lack of financial data scientists

There are plenty of niche positions in financial data science, and big banks are already scaling up teams of financial data scientists that can work cohesively to add business value. Since it’s incredibly rare to find a senior financial data scientist, many are recruiting candidates with specialized skills that can help drive data-driven value.

The skills gap in data science is clear. Universities and colleges have been slow to offer this course of study, which is why many opt for online courses and bootcamps to learn data science. For the financial data scientist, this rings particularly true. There are limited courses and training for this particular position. Many combine financial study or training with DIY data science course work, pulling together different skills to become a financial data scientist. Given the lack of finance-specific data science courses, it’s no wonder this position is in high demand.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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