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What Is a Data Scientist?

Across industries and sectors, data scientists analyze data sets, often from new or alternative sources, apply coding and programmatic skills alongside modern analytical techniques to databases to seek meaningful patterns and insights, and communicate relevant findings to key stakeholders. Within the investment industry, data scientists are increasingly needed to analyze big data and find means to generate alpha. Data scientists in finance provide support and advice to relevant teams within the organization, including investment teams, and develop tools and dashboards to improve the investment process.
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How to Become a Data Scientist in the Investment Industry

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.

To become a data scientist, in addition to getting a Bachelor’s degree in a relevant field, you may want to consider specializing in a specific industry or skillset to differentiate yourself across candidates. For example, a data scientist can specialize in:

  • Big Data
  • Machine learning
  • Statistical analysis
  • Data engineering
  • Data visualization
  • Industry-specific data analytics (such as financial data analytics)

There are certifications available to help you specialize in a specific field. However, if you are not interested in specializing, you can become a generalist by attaining a broad range of skills that can apply to various job functions.

Is a Data Science Career Right for Me?

Because data science within finance shares many characteristics with traditional performance analysis, such as the need for quantitative skills, many of the qualities needed for other roles also apply to this field. In addition, data science also requires an aptitude for mastering and applying new technology. Some investment professionals pursuing careers in data science have found that so-called soft skills can become even more important for success. Data science roles require adaptability, curiosity, and collaboration, among other personal traits.

  • Key Skills

    In general, investment professionals in data science and other fintech careers need the following skills to be successful:

    • Problem-solving mindset
    • Business strategy
    • Communication skills
    • Quantitative analysis

  • Technical Skills

    • Python programming
    • R programming
    • Data visualization
    • Machine learning techniques
    • Artificial intelligence (AI)
    • SQL
    • Database architecture

  • Qualifications

    A typical data science role will require a Bachelor’s degree in data science or a related field, such as computer science, math, or statistics. Additionally, you should also learn technical skills, such as programming languages and database architecture, through coursework or supplementary certifications to improve your marketability.

Other Possible Career Tracks in Fintech

Data science within finance encompasses a wide range of opportunities for investment careers. Areas with a technology focus include cybersecurity, data science, machine learning, and AI, among many others. Roles that require financial or investment expertise include blockchain development and quantitative investing.

How Can the CFA Program Help Me?

Data science in the investment industry is creating new opportunities for investment careers. In addition to roles that are directly involved with the investment process, there is also a growing demand for professionals who can apply their investment expertise to support the technology platforms and business models developed by technologists and others who lack investment training. The CFA Program provides the foundational knowledge of the investment industry to supplement and demonstrate how your background in data science is applicable within the world of finance.

Explore whether CFA Program is the right choice for your next career steps

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