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What Does a Data Science in Finance Scientist Actually Do?

Data scientists are important to almost every industry, but finance is one area where they are especially important. Find out here what role data science plays in the finance industry.

As the world’s economy’s backbone, the finance sector has known for a long time how important big data is for making profitable decisions and taking calculated risks.

As one of the first industries to use big data, the industry has changed everything from how the stock market works to how fraud is found and even how the customer experience is made.

Because big data has so many possible uses, there is a huge need for skilled scientists in the finance industry.

But what is a financial data scientist, and what do they do? We’ll answer all of these questions and more in this post. Want to go straight to a certain topic? Use the menu you can click on:

What is a Financial Data Scientist?

As one of the most profitable industries in the world, global finance was one of the first to realise that big data could be used to make money. This doesn’t come as much of a surprise, since banking has always been about trying to predict how the market will change so that you can make the best investments and get an edge over your competitors.

Data analytics, on the other hand, is all about trying to make predictions. Since banks and other financial institutions have access to our information, like market metrics, transaction data, and detailed customer profiles, the two go well together.

But the sheer amount of big data that the finance industry collects makes it hard. How to best use all this unstructured data is one of the biggest problems (i.e. data that are completely disorganised and lack a coherent model).

Banking is a very complicated field in and of itself, which adds to the confusion. There are a lot of mergers, acquisitions, complicated products, and changing rules in this industry. Putting together big data and a world that is always changing requires a lot more skills than a typical data analyst would have.

Data Science in Finance

Enter financial data science. With skilled experts in charge, financial service providers can simplify complicated processes and break down the silo culture that is common in the field. Instead of just collecting, mining, and making sense of data like data analysts, financial data scientists are experts in their field with a deep understanding of their field.

A financial data scientist’s job can include anything from finding fraud to coming up with personalised ways to help customers. It could mean building complex data warehouses or making algorithms that automate important financial interactions.

It could even include all of these. This makes it a very different field with lots of room for career growth. Also, as we’ll see in a bit, it pays very well. However…

What Does a Data Scientist in Finance Do?

Now that we have a general idea of how data science in finance came to be and why it is so important, what does a financial data scientist do on a daily basis? In reality, these can cover a wide range of things. Depending on where they work, there are many different ways they do things. Some of these areas are:

  • Risk management
  • Fraud detection
  • Customer experience
  • Consumer analytics
  • Pricing automation
  • Algorithmic trading

The details of these jobs are very different. A data scientist’s main job is to come up with ways to collect and store data, mine it for insights, and then come up with and implement strategic solutions to important problems (like those just listed).

The tasks and responsibilities expected of financial data scientists are a great way to picture what they do on an average day. Even though it’s hard to generalise, here’s a taste of what you can expect based on real job descriptions for financial data science.

General Responsibilities of a Data Scientist in the Finance Sector:

  • Collecting strategic data and planning, engineering, and documenting complex data infrastructures.
  • Using techniques for data modelling to bring unstructured and semi-structured data together.
  • Using natural language processing (NLP) and computer vision to look at data that is not organised or is only partially organised.
  • Working closely with different teams, from DevOps to top management, to find problems and come up with solutions that are based on data.
  • Using quantitative analysis to get insights, turning those insights into workable solutions, and then putting those solutions to good use (while measuring outcomes).
  • Using existing data to train machine learning models and making prototypes of systems to test out new ways of doing things.
  • To help manage the data analytics and machine learning processes, they come up with and code new algorithms from scratch.
  • Clear communication with different parts of the business and helping people who are less experienced.

Examples of Role-specific Responsibilities

  1. Finding new ways to analyse risks or ways to automate the process of risk management (risk management).
  2. Designing and building high-performance identity verification applications that can stand up to aggressive fraud attacks (fraud detection).
  3. By analysing how products are used and how customers act, business units can make suggestions to improve the customer experience (customer data).
  4. Strong knowledge of how credit cards work, accounting, and SOX controls, which are a type of error protection procedure (consumer analytics).
  5. Keeping track of trade algorithms and changing them so that they can work with other trading platforms (algorithmic trading).

For the sake of this post, we chose not to get into the technical details, which are likely to be more confusing than helpful at this point (although you can get an idea in section four). Even though this list isn’t complete, it shows how varied a financial data scientist’s job can be.

What Experience Do You Need to Become a Data Scientist in the Finance Industry?

Next, what skills and experience do you need to become a data scientist in the finance industry? There are many ways to get into the field, which is a good thing. You could start as a data analyst, move up in your career, and improve your skills as you go. But if you want to work for a bank, a fintech startup, or an insurance company, you’ll probably need these skills and experiences:

  • A bachelor’s degree or higher (preferably a master’s or doctorate) in math, statistics, computer science, or a related field.
  • Deep knowledge and understanding of the financial industry and its rules (at least for the area you’ll be working in, like risk assessment or insurance claims).
  • Knowledge of a wide range of general data science tools, such as machine learning algorithms, deep learning, data analytics, and natural language processing.
  • You need to know both the theory and how to use it to make your own statistical models.
  • Expertise with big data technologies, such as clustered computing architectures like Apache Spark, Hadoop, etc.
  • Ability in multiple programming languages, especially Python and R, and maybe others, like JavaScript or C++, depending on what you want to do.
  • Understand how to work with unstructured or semi-structured datasets and be able to do so.
  • Knowledge of important financial systems, such as SAP, Oracle, SWIFT, etc.
  • Knowledge of the many key data providers in the finance industry, such as Acuris, Bloomberg, Moody’s Analytics, Thomson Reuters, etc.

Data Science in Finance

It can take a few years to learn or improve these skills. So, your next question might be whether or not it’s really worth your time. The next part might help you make up your mind.

How Much Can Data Scientists in the Finance Industry Earn?

The pay you can anticipate as a data scientist will depend on your experience level, your area of specialisation (such as risk assessment vs. customer experience), and the company you work for. Even the title of the position itself will matter.

Although this makes it difficult to determine a precise compensation, by examining the median salaries for various data roles, we may get an idea of how much you could be able to earn in the banking industry.

These are the typical earnings in the US for positions involving financial data analytics, according to the salary comparison website Payscale. These were accurate as of the start of 2022, but you may click on each wage for a more recent estimate (along with a more in-depth breakdown):

  • Business Analyst, Finance/Banking: $66,700
  • Senior Analyst, Finance: $78,300
  • Senior Financial Analyst: $82,500
  • Senior Credit Analyst: $69,700
  • Senior Compliance Analyst: $74,800
  • Budget Analyst: $62,350
  • Quantitative Analyst: $85,500
  • Risk Management Analyst: $70,800


We looked at the ins and outs of data science in the financial sector in this piece. We discovered that:

  • Financial firms have access to enormous volumes of data, which financial data scientists work with. High-stakes business decisions are based on this.
  • On addition to risk management and fraud detection, financial data scientists also work in automated pricing and algorithmic trading.
  • In order to succeed, you’ll probably need a master’s or doctoral degree, which is a higher level of expertise than what is often required of data analysts in the banking industry. A data analytics certification, however, is a great place to start.
  • Approximately 11% of data scientists have decided to focus on the financial industry.

If you decide to pursue a career here, you’ll be highly compensated, with the chance to make up to $127K annually.

Aaron Rigby
Aaron Rigby
I'm a skilled writer who puts my heart and soul into my work. I've been working as an author at a news degree for the last 2 months. I love to spread my knowledge, which I gain through newspaper magazines and the internet.


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