Required Qualifications ● Pursuing a degree in Data Science, Statistics, Computer Science, Finance, Economics, or a related field. ● Basic proficiency in Excel or Google Sheets; familiarity with Python, R, or SQL is a plus but not required. ● Strong analytical and problem-solving skills, with the ability to work with datasets and extract insights. ● Clear written and verbal communication skills; able to present findings in simple, business-friendly language. ● Detail-oriented and organized, with the ability to manage multiple tasks. ● Interest in applying data to support financial services, wealth management, and business strategy. Overview Quantum Financial Advisors (QFA) manages over $600 million in client assets and increasingly relies on data-driven insights to enhance client service, investment research, and business operations. As a Data Science Intern, you will help clean, organize, and interpret financial and business datasets, create reports that highlight key trends, and support projects that integrate data into consulting and advisory work. This role emphasizes practical analysis, collaboration, and clear communication of findings—bridging technical data work with real-world financial applications. Key Responsibilities ● Organize and clean financial and business datasets for analysis. ● Conduct basic statistical analysis and highlight patterns or trends. ● Prepare visualizations (charts, tables, dashboards) to support client reports and internal projects. ● Summarize third-party datasets (fund information, benchmarks, economic data) into digestible insights. ● Document methods and findings in clear, professional notes for use by consultants and advisors. ● Collaborate with finance, consulting, and operations teams to align data outputs with business needs. ● Support ad hoc research projects that apply data science methods to wealth management. Areas of Focus ● Financial Data Cleaning & Structuring ● Descriptive Analytics & Reporting ● Data Visualization & Dashboarding ● Market & Fund Research ● Business Process Support through Data ● Communication of Findings to Non-Technical Stakeholders Learning Outcomes ● Gain experience applying data science concepts in a financial advisory environment. ● Learn how to turn raw data into actionable business insights. ● Build practical skills in Excel, visualization, and introductory analytics. ● Develop awareness of how financial firms use data for decision-making, compliance, and client communication. ● Strengthen collaboration skills by working across finance, consulting, and operations teams. ● Improve the ability to communicate technical data clearly to non-technical audiences.