What is a Data Scientist?
A data scientist is a professional who leverages information, statistics, and computer science to extract insights from large datasets. They play a vital role across various industries and sectors as employers rely on analytics to make better decisions.
What does a Financial Data Scientist do?
Depending on the role and organizational structure, a data scientist in the finance industry may be responsible for several functions. Their work and day-to-day tasks may include:
- Cleaning and preparing data for analysis
- Analyzing data for trends and correlations
- Building predictive models
- Supporting big data projects
- Designing and developing databases
- Applying coding and programmatic skills to projects
- Organizing, interpreting, and visualizing data
- Communicating results to stakeholders
A full-stack data scientist may be responsible for the entire process of transforming raw data into actionable insights.
How to become a Data Scientist in the investment industry
Becoming a data scientist in the investment industry often requires technical skills, industry knowledge, and formal education. Job descriptions for financial technology (Fintech) roles generally require a bachelor's degree in computer science or a related field, proficiency in programming languages, and an aptitude for research and analysis.
Many investment professionals in the field of data science consider so-called soft skills, such as communication and business acumen, to be essential for securing employment.
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.
Career paths for Data Scientists in finance
Data science within finance encompasses a wide range of opportunities for investment careers. Jobs opportunities, roles, and titles for data scientists include:
- Financial Analyst
- Big Data Analyst
- Risk Manager
- Machine Learning Specialist
- Data Visualization Expert
- Business Intelligence Consultant
- Research Analyst
- Natural Language Processing Specialist
Related roles and opportunities
Data scientists in the finance industry may assume various roles and collaborate with professionals such as data engineers, database architects, database administrators, financial statisticians, and software developers. Additionally, they may specialize in one of several fields where data science and finance converge such as cybersecurity, machine learning, artificial intelligence (AI), blockchain development, and quantitative investing.
Which industries are hiring Data Scientists?
Industries such as finance, retail, healthcare, manufacturing, agriculture, oil and gas, marketing, and public sector organizations have a high demand for data scientists. Financial institutions, for example, rely on data scientists to help assess and manage risk, create reports to communicate findings, and build predictive models to forecast trends better.
Data Science Resources
If you're interested in learning more about data science, we have a collection of research papers to help you get started.
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