The rise of big data and technology has redefined much of how the finance industry operates. From the creation of mobile banking applications to instant payment services to cryptocurrencies, each new technology has changed the way we use and send money and manage our finances. As these technologies grow, so do the job opportunities for data scientists in the finance and investing industries. Careers in the field include cybersecurity analysts and application developers. This is an opportune time for data scientists to begin a career in financial technology!
What is Financial Technology (FinTech)?
Financial Technology, or FinTech, encompasses the innovations and industries that have revolutionized finance, investing, and banking. FinTech focuses on the specific technologies that enable the smooth flow of financial data, easy consumer interaction, and reliable predictions of economic trends. FinTech includes mobile banking applications, apps that allow users to transfer money to each other, platforms for investing and algorithmic trading, and the management of cryptocurrencies. Consequently, FinTech is the primary impetus for learning data science for finance.
The Demand for Data Scientists in Finance
The demand for data scientists in finance is steady across industries. Data scientists in finance can apply their data collection, analysis, and visualization skills to multiple industries that use information about financial trends and patterns. For example, data scientists use SQL for financial analysis to pull data from database management systems, using it to perform analyses and develop insights from financial data trends. Developers also use Python for financial analysis to develop automated models and artificial intelligence for financial technologies like banking apps.
Financial data scientists engage in the large-scale collection of financial data–including accounting, financial transactions, economics, and investing–from the past and the present to make predictions. Data analysts can predict future financial trends and use predictive analytics to mitigate the risks for banking and financial institutions of lending money and making investments. Simply put, data scientists and analysts in the finance industry pull information from data and then present that information for decision-making.
Top Finance and Technology Careers for Data Scientists
Financial Analyst
Data scientists working in finance are generally called Financial Analysts. As financial analysts, data scientists work on retrieving big data from database management systems and analyzing data using programming languages like R or Python. Many financial analysts also have advanced data analytics training, specializing in predictive and prescriptive analytics and using business intelligence tools. Depending on the industry, financial analysts apply their knowledge of data analytics and business intelligence to ensure that banking and financial institutions make good decisions. It is also common for financial analysts to work as part of a data science team, including in many FinTech careers listed below.
Financial Risk Manager (FRM)
Financial data in the banking, investing, and lending industries is used primarily for risk assessment and management. Financial risk managers (FRMs) evaluate and respond to financial and business risks. Whether the risk is lending money to a company or individual, or the risk of investing, the risk manager draws on historical and financial data to determine the soundness of a business decision. FRMs play an essential role in managing and analyzing financial data using statistical analysis and economic theories and crafting models and protocols for evaluating risk within a company or industry. Outside of financial data, risk management can also overlap with areas like cybersecurity. This risk is assessed by analyzing a system's safety in the face of a cyberattack.
Portfolio Manager
Investment firms and hedge funds are the primary spaces where portfolio managers handle the financial data of an individual or institution. Portfolio managers specialize in financial planning and building investment portfolios, working one-on-one with clients or as part of a team advising a company. Investment portfolios are a curated dossier of all financial investments held by an entity, including stocks and bonds or even cryptocurrencies. And while many people self-manage their investment portfolios, portfolio managers analyze market trends and evaluate and create investment strategies based on data forecasting. Therefore, data scientists interested in careers as portfolio managers should build their knowledge in law, business, and economics and obtain the certifications required to pursue this role.
Cybersecurity Analyst
Cybersecurity analysts engage with financial data management differently than finance and investment analysts and managers. The safety of a client’s financial holdings and personal information is one of the primary concerns in the finance and technology industry. Every institution that handles financial data, from the local bank to the Internal Revenue Service (IRS), uses the internet and various technologies to manage consumers' financial data. Therefore, cybersecurity for data scientists in finance and technology focuses on evaluating and improving the networks, systems, and technologies that these institutions have created to manage financial data. Cybersecurity analysts must have training in database management and design and knowledge of network security, automation, and machine learning to create safer systems and applications.
Software or Application Developer
Software or application developers have the most technology-focused roles in the financial technology field. Developers build products that improve the user experience of managing and accessing financial data. An improvement may be as simple as enhancing the mobile application, making it easier and faster to invest and trade stocks or as complex as adding a real-time analytics feature to the app.
Data scientists interested in development can pair their knowledge of programming and database management with engineering. Software engineering for data scientists focuses on front and back-end development skills and user experience (UX) design, which are required when developing an application or improving upon existing financial technology.
Want to Learn More About FinTech?
Data and technology are redefining the finance industry, and FinTech is a worthwhile path for data scientists to enter the field. Many of Noble Desktop's data science classes include instruction in data science for finance. Beginner students can take the FinTech Bootcamp to learn foundational skills such as using Python for financial analysis and automated machine learning. More advanced data science students should consider the Python for Finance Bootcamp, which teaches Python's financial libraries and data analysis. Students and professionals interested in specializing in this industry should also check out Noble Desktop's FinTech Bootcamps, offered in a remote live-online format.