The financial sector is one of the most promising fields for data science. Data analysis technologies like machine learning have substantial potential in the industry, and the rise of financial technology (fintech) provides even more opportunities for data scientists.
As data science operations within finance and fintech have grown, Python has emerged as an industry favorite. Here’s a closer look at the evolving role of Python in the financial sector and what it means for data professionals.
Python in Finance and Fintech Today
There are nearly 1,500 open positions on eFinancialCareers’s database – a financial employment site – that require Python knowledge or skills. That’s more than any other programming language. Developers in the industry have made their position clear: they want people who know how to work with Python.
Industry leaders have taken to the language, too. Bank of America, J.P. Morgan, and Citigroup have all used Python to build their fintech applications. Some of these large investment banks further emphasize their desire for Python skills by re-skilling and up-skilling employees in the language.
How Python Became Finance’s Language of Choice
Python’s rise to prominence in the finance industry is easy to understand when looking at what makes it unique. First, the language is famously simple, making it easy to write and deploy.
While Python isn’t the fastest language in terms of CPU cycles, it offers a faster time to market. Since you can write and debug Python code faster than many other languages, you can launch products more quickly. That’s a crucial advantage in an industry as competitive and rapidly advancing as finance and fintech.
Python’s general popularity also makes it ideal for fintech applications. The number of data science teams per finance firm grew by 260% between 2018 and 2020, but ongoing tech talent shortages are threatening. Favoring an already-popular and easy-to-learn language enables easier upskilling to sustain that growth.
Python’s vast open-source community and abundance of libraries and tools also benefit finance organizations. Third-party integrations and interoperability are crucial for the customer service-focused fintech industry, and these resources make that easier.
The Future of Python in Finance and Fintech
Python will likely keep growing in finance and fintech circles. As the industry embraces more data science applications, its demand for Python’s simplicity, popularity, community, and speed will rise.
Machine learning, in particular, has significant growth ahead of it in finance. Currently, robot financial advisors lack the critical thinking necessary for widespread adoption, but reliable models could provide competitive insight and cost reductions. Machine-learning development in the industry will rise to meet this need, and Python will grow with it.
Smaller fintech companies will continue to offer more affordable and disruptive alternatives to traditional banking. In such a fast-growing, increasingly crowded market, fast development times and interoperability are crucial. As a result, these smaller firms will spur growing demand for Python skills, too.
What This Means for Data Scientists
Python’s surging popularity in finance and fintech has substantial implications for data scientists. This industry is one of the best employment opportunities for data scientists, especially as its data operations grow in the future.
Experts predict 59% of new data science jobs to appear in finance, IT, insurance, and professional services. Finance and insurance lead that group, accounting for 19% of all new jobs. As opportunities in this industry grow, having the skills and experience the sector needs will become increasingly important.
Finance is leading the data science jobs market, and data science jobs within the sector rely heavily on Python. Consequently, learning to use Python and staying on top of emerging Python trends could make you a more promising candidate in the future. As fintech surges and conventional banks embrace machine learning, demand for Python-capable data scientists will grow even faster.
Use the Right Tools for the Job
Data science success often relies at least in part on using the right tools for the task at hand. In the finance and fintech industry, that means using Python. The language’s speed, community, ease of use, and popularity make it the ideal foundation for projects in this sector.
Finance and fintech present some of the most enticing employment opportunities for data scientists, and that will continue to be the case. Gaining experience in Python now could help advance your career in the future.