Preferred Background •Major in CS, Data Science, AI/ML, or Engineering •Familiarity with Python and common ML libraries •Interest in applied AI, ML systems, or AI-enabled applications
Internship Description
This internship provides project-based learning exposure to quantitative analysis, focusing on statistical modeling and time-series analysis applied to financial and economic datasets. Participants will work on guided quantitative modeling projects, learning how mathematical and statistical methods are used in analytical and finance-related contexts.
Activities & Exposure
• Practice implementing regression and forecasting models using Python or R
• Explore time-series analysis and basic econometric techniques
• Conduct Monte Carlo simulations and introductory optimization analysis
• Visualize analytical results using standard plotting libraries
• Summarize model performance, assumptions, and limitations in reports
Outcomes
• Practical understanding of quantitative modeling workflows
• Experience evaluating model accuracy and performance metrics
• Improved ability to explain quantitative results clearly
Requirements
Preferred Background
Background in Mathematics, Finance, Statistics, Engineering, or related fields
Proficiency in Python or R and common statistical packages
Understanding of probability, statistics, and basic econometric concepts
Interest in quantitative analysis or finance-related roles