Global Data Science Forum

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By Tim Stone posted Sun May 29, 2022 02:26 PM


Having the qualifications and the correct orientation may not take you far unless you are not ready to do some reality inspection.

The fintech industry is one sector that stirs zillions of data every day, which is one of the prior roles of the industry. Data analysis has been its core function for ages. With cutting-edge technologies, it is only that the way the information is perceived and processed has transformed. Fintech is growing in bounds every year and so is the need for data analysts. Paired with current technologies such as the IoT, Blockchain, AI, and ML, it efficiently speeds through the rough winds of innovation and competition. Fintech is basically a candidate’s need. That means the aspiring Fintech data scientists have the upper hand in selecting a job that fits best their capabilities and mental predisposition. Thus, becoming a Fintech data scientist shouldn’t sound like a challenge when numerous options are waiting to be filled. Yet, having the capabilities and the right orientation may not take you far unless you are not willing to do some validity check.

How financial data scientist varies from other data scientists:

Finance being the first sector to put data to predict market trends, adjusting to data science has only allowed it break the silo culture, which is once in a time incidence for other sectors. Given the finance sector’s ever-changing geography and regulatory measures, simply gathering, analyzing, and making meaning of data wouldn’t be acceptable. A finance data scientist’s job goes beyond these traditional functions. From building complex data warehouses to creating algorithms and indicating fraud, a finance data scientist’s job can be any mixture of these tasks. Their day-to-day functions contain:

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

Here is the caveat – though there is high need for data scientists in the fintech industry, it presently employs the least portion of data scientists in the industry. It has instantly to do with the skills a finance data scientist should have to provide the services.

How to become a financial data scientist

Here are some actions to follow for how to become a financial data scientist:

Earn a bachelor's degree

Gaining a bachelor's degree is your first stage in how to become a financial data scientist. You need to select a major with a powerful focus on math content. Consider choosing a major like:

  • Science
  • Computer science
  • Economics
  • Statistics
  • Mathematics
  • Physics
  • Engineering

Discover how to program

    Financial data scientists need to learn how to program in a variety of languages. Gain knowledge working with compiled and analyzed programming languages. Become familiar with languages like:

    • C/C++
    • Java
    • MATLAB
    • Python
    • S

    Develop database skills

    Financial data scientists use databases to recover and organize data. It's essential to understand how to use various relational database management systems or RDBMS. Gain knowledge working with classic RDBMS such as:

    • MySQL
    • PostgreSQL
    • SQL server

    Learn how to take series data

      Develop talents to help you handle large data sets specifically from financial streams. Learn how to understand and manage raw data. This will allow you to learn how to interpret data to make suggestions to organizations about financial conclusions.

      Consider making a master's degree

      Evolving a financial data scientist does not always need a master's degree. Nevertheless, earning a master's degree or MBA may help supply you with more opportunities or qualify you to advance to senior roles. Consider pursuing your master's degree in economics, finance or statistics.

      Consider making a certification

      Consider making a certification to verify your skills. Research your options for pursuing certifications connected to data science or the financial enterprise. These certificates may demonstrate your ability to help provide you with more career opportunities.

      Skills for a financial data scientist

      Here are some talents for a financial data scientist to develop:

      • Using databases
      • Retrieving data using SQL and NoSQL
      • Working with big data
      • Data frameworks like Spark, Mapreduce and Hadoop
      • Data munging
      • Data analysis
      • Quantitative methods
      • Computer programming languages, such as Python and R programming
      • Machine learning
      • Domain knowledge
      • Analytical thinking skills
      • Problem-solving skills
      • Predictive analytic skills
      • Understanding of probability and statistics
      • Understanding of financial principles
      • Experience working with financial markets
      • Ability to communicate mathematical ideas
      • Ability to apply mathematical exercises to solve commercial problems
      • Ability to work in high-pressure conditions
      • Ability to form connections with stakeholders

      Financial data scientist salary

      The national intermediate salary for a financial data scientist is $121,050 per year. Financial data scientist earnings vary based on a range of factors. These aspects may include years of background, education, type of organization and location. For instance, the national average salary for a senior financial data scientist is $152,067 per year.

      Financial data scientist job description

      Here's a sample job description for a financial data scientist:


      The financial data scientist leads developing strategies, models and operational procedures for the organization's credit card effects. The data scientist will create analytic and quantitative models and applications to help with tracking, analyzing and writing on risk metrics. This role needs experience with possibility and statistics, applied mathematics, physics or related course. The financial data scientist documents to the senior financial data scientist and aims to develop theory and mathematics for models.


      • Developing models
      • Developing intellect applications to help in decision making
      • T**esting statistical instruments and packages
      • Working with risk management experts to identify and resolve issues


      • Master's degree in connected field preferred, bachelor's degree and relevant knowledge accepted
      • Expert knowledge of statistical techniques
      • Expert knowledge of ML and Artificial Intelligence methods
      • Experience developing business data queries in SQL
      • Experience creating complex data visualizations
      • Experience creating complex data models in Python, R or SAS