Data Science in Finance [+ Career Guide]

5 min read

Consider the different types of data you may come across within a financial institution: assets, transactions, investments, cash flow, consumer behavior, market trends and metrics, risk assessment, loans — the list goes on. 

But it’s how a company manages, interprets and utilizes the data that matters, which is where the role of a data scientist comes in. This increasingly popular position is becoming a must for many businesses and organizations around the world but especially in the financial sector, where data has helped transform an industry with an estimated annual revenue in 2021 of $4.85 trillion

How Is Data Science Used in Finance?

Data science is an important field that Amazon explains as “the study of data to extract meaningful insights for business.” This approach includes everything from artificial intelligence and computer engineering to statistics and math, and it’s used across all types of industries — but especially within financial sectors.

Examples of data science applications in finance include risk analytics, real-time analytics and the ability to detect fraud. 

For example, as Finance Train explains, data science can quickly show potential issues or problems, which means managers can help mitigate these risks before they manifest into larger challenges. In addition, data from a variety of courses, including social media, can also help financial institutions make more accurate risk assessments when it comes to borrowers. Monitoring and analyzing consumer behavior will also help companies in targeting potential markets and clients.

Here’s another example of data science in finance — and one that you’ve likely come across. Has your bank ever flagged your credit card due to multiple or unusual transactions? This is due to real-time analytics, which compares your “typical” behavior and looks for unusual charging patterns or an unusual location from which your card was used.

Additional applications of data science in finance include: 

  • Customer data management
  • Personalized services 
  • Algorithm trading

What Is a Financial Data Scientist?

CFA Institute defines financial data scientists as skilled professionals who “provide support and advice to relevant teams within the organization, including investment teams, and develop tools and dashboards to improve the investment process.” This also includes analyzing data sets and using coding and tools to decipher patterns and insights among vast amounts of information. 

If you’re interested in this type of career, you may also come across two related positions — financial analyst and quantitative analyst. Though they have crossover with data scientists, there are some distinct differences. 

A financial analyst examines data to identify opportunities and makes recommendations to clients about their portfolios and buying and selling individual investments. They tend to look closely at historical and current data and the latest financial trends. While it’s a position that does “crunch the numbers,” it’s also one that includes managing client relationships, writing reports, and making recommendations based on the latest financial information. 
A quantitative analyst is a professional who specializes in “the design, development, and implementation of algorithms and mathematical or statistical models intended to solve complex financial problems,” according to the Corporate Finance Institute.

What Does a Data Scientist in Finance Do?

The specific responsibilities will depend on the position, but in general, a data scientist will “develop processes for collecting and storing data” and mine the data for insights before “developing and delivering strategic solutions to key problems” according to CareerFoundry. 

Here are some real-world examples pulled from data scientist job postings in the financial sector on LinkedIn:

  • Maintain models that drive credit decision automation.
  • Ensure adherence to models’ risk management guidelines. 
  • Develop, implement, maintain, and run models for predicting business trends.
  • Provide analyses that drive business decisions and business planning. 
  • Demonstrate to internal and external stakeholders how analytics can be implemented to maximize business benefits. 
  • Operate on big financial and non-financial data using modern parallel data analytics tools (e.g. Spark)
  • Manage loan portfolio for all prospects, leads, and customers by monitoring trends, identifying opportunities, developing and presenting recommendations, implementing the strategies, and measuring impact.

Education and Experience Needed to Become a Financial Data Scientist

For most positions, you typically need at least a bachelor’s degree in computer science, data science, or a related field. Many employers, however, prefer a master’s degree or even a Ph.D. 

Associate data scientists, or entry-level positions, may only require a year or two of experience, which may be achieved through an internship, bootcamp, or master’s degree program. 

Senior-level or managerial positions, however, typically need at least 5–10 years of experience.  

According to CFA Institute, “a data science career path requires competence in computer science, programming, and mathematics. For data scientists who wish to work within the investment industry, a broad understanding of financial markets, financial instruments, and investment products is also highly valuable.”

Hard and Soft Skills Needed

A data scientist, regardless of the industry in which they work, must possess the following hard and soft skills:

  • Knowledge and understanding of mathematics, statistics, and databases
  • Proficiency in programming languages (Python, R, SQL, etc.)
  • Domain knowledge
  • Data wrangling
  • Data visualization
  • Machine learning 
  • Good communication
  • Dedication to lifelong learning 

It’s usually recommended that data scientists in the financial sector have a key understanding and interest in the financial industry and markets. 

To give you an idea of what finance companies are looking for, here is a compilation of hard and soft skills found on recent real-life job postings (note: skills are subject to change depending on the job posting):

  • Experience with at least one querying language (e.g. SQL) and/or one scripting language (e.g. Python); being comfortable working with large, complex, and potentially messy datasets.
  • Experience with building machine learning models and visualization tools such as Tableau/Periscope is a plus.
  • Advanced statistical knowledge; ability to go well beyond the basics of bias, independence, significance testing, confidence intervals, and correlations.
  • Strong collaboration skills having worked with business stakeholders at different levels of seniority and technical proficiency.
  • Experience with insurance or consumer financial data is a plus.
  • Demonstrated ability and interest in learning new algorithms and tools, including optimization, natural language processing, and graph analysis.

Finance Data Scientist Salary

Salary will depend on many factors, including the specific job responsibilities, company or organization, location, and how much experience is required. In general, salaries for data science positions are high, with many data scientists earning six figures. 

Here are some national averages to give you a broad sense of what to expect. Please note that the numbers will fluctuate as information is updated based on changing data. 

  • National average of $128,851 with a range of $83,000 to $203,000 (Glassdoor)
  • National average of $95,909 with a range of $25,000 to $179,000 (ZipRecruiter)

Top Companies Hiring Financial Data Scientists

There is no shortage of organizations and businesses that are looking for skilled financial data scientists. From larger, well-known enterprises to smaller start-ups, there are plenty of opportunities — including global, remote, and hybrid options. 

A recent search for data scientist positions in the financial sector on LinkedIn revealed more than 20,000 results. The following is a list of some of the top companies that are hiring for these types of positions: 

  • JPMorgan Chase & Co.
  • PayPal
  • Fidelity Investments
  • S&P Global
  • Edward Jones
  • American Express
  • Barclays
  • Mastercard
  • Liberty Mutual Insurance
  • John Hancock
  • Discover Financial Services
  • USAA
  • Prudential Financial
  • Vanguard

Interested in Advancing Your Data Science Career In Finance? 

We recommend downloading our free checklist — 7 Questions to Ask Before Selecting an Applied Data Science Master’s Degree Program. This resource provides insights that are designed to help you select a program that will best help you achieve your career goals. 
If you have any questions or would like more information specifically about USD’s Master of Science in Applied Data Science program, please contact us, and you will hear from one of our enrollment advisors shortly.

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