Whether it’s fraud detection, risk monitoring, or helping banks navigate a recession, data scientists play a valuable role in the finance industry and can steer organizations away from financial catastrophes toward financial opportunities. Learn more about the roles, responsibilities, and salaries of financial data scientists here.
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If there’s one industry that understands the importance of data scientists, it’s finance. Well before data science was even a recognized industry term, financial institutions saw the need for analytical, curious, and solution-oriented specialists who could wrangle large amounts of customer and financial data for statistical analysis, forecasting, and risk analysis. With a growing number of transactions now taking place electronically and staggering amounts of financial data being generated every day, there is no shortage of opportunities for data scientists—especially those who like playing with numbers.
At its core, data science in the financial sector isn’t too different from data science in other fields—the ultimate goal is to tease out meaningful and actionable insights from collected data. But the stakes for data scientists can often be higher in the finance industry than it is in others, with companies relying on the findings of their analytics teams to make important decisions. In fact, it’s not unusual for a data scientist’s work to mean the difference between an institution surviving a period of tumult or failing entirely.
In high-stakes situations such as recessions and financial crises, data scientists play a pivotal role in taking the guesswork out of make-or-break decisions. Where other positions within an organization might be on shaky ground during an economic downturn, data scientists tend to see an increase in demand for their services because financial organizations rely on analytics to help them identify where to cut costs and increase efficiencies. “Executive leadership will most certainly look to the analytics/data science group for guidance during a recession because analytics has a proven track record for adding data-driven value,” said John Morris, managing director of operations decision science at Delta Air Lines.
Day-to-day, financial institutions rely on data scientists to perform core functions such as fraud detection, risk modeling, and identifying ways to improve the customer experience. In the case of fraud detection, data scientists use machine learning to identify anomalies in transactions so they can flag potential instances of identity theft or misuse of funds. When it comes to risk modeling, the work of data scientists determines layman-facing instruments such as credit scores, and helps banks and other financial institutions decide whether a loan applicant poses a credit or investment risk. And data scientists can improve the customer experience by helping banks deliver targeted offers, as seen in the case of YES Bank—the fourth largest private sector bank in India—whose data scientists analyzed customers’ debit card usage to ensure that they only received offers relevant to their interests, resulting in a 44% spending increase among those targeted customers.
Most data scientists bring to the table technical skills such as knowledge of probability and statistics, data visualization, machine learning and AI, and proficiency with Python and SQL. And while these skills might help a person parse through troves of information, the finance industry expects its data scientists to also have domain expertise, strong communication, and the ability to forge relationships with stakeholders.
On the domain expertise front, data scientists should be familiar with the specific field of data they are looking to analyze, whether it’s hedge funds, investment banking, fintech, or retail banking. For example, a data scientist analyzing investment risk data would be expected to understand economics, risk analysis, portfolio management, and financial markets. Likewise, someone analyzing loan data would be expected to know how loans work, how a particular financial institution manages its loan portfolios, and industry risk assessment standards.
On the communication front, data experts say that even though the value of analytics within an organization often speaks for itself, strong leadership and an ability to advocate for data science are crucial skills to have, especially during economic downturns when every department is on the cutting block. The best data science leaders are able to show the positive effect their work has on business outcomes and help non-technical managers and executives understand what they do. “[They] got to where they are because of their ability to communicate their value (on top of technical prowess) to the overall organization,” said Charles Thomas, who has led data and analytics groups at USAA, Wells Fargo, and General Motors.
Other common responsibilities of finance industry data scientists include:
Data science across the board is experiencing a boom, and the financial sector continues to see high demand for data scientists. In 2019, LinkedIn ranked data science as the most promising job in the U.S. based on job openings, salary, and career advancement opportunities, and reported a 56% year-over-year increase in job growth.
The salary of a data scientist is typically determined by education, years of experience, location, and organization type. As of 2020, the average salary of an entry-level data scientist in the finance industry is around $101,175. The average salary of a senior-level data scientist in the finance industry is around $135,514.
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