Quantitative Analyst Intern

Clockles Technology; Dover (DE); On-site; USD 25,000 - 39,000; Part time

Job summary

Clockles Technology in Dover, Delaware offers an internship that provides project-based learning in quantitative analysis, focusing on statistical modeling and time-series analysis applied to financial and economic datasets. You will work on guided modeling projects using Python or R, perform Monte Carlo simulations, visualize results, and document performance and limitations in concise reports.

Preferred background includes math, finance, statistics or engineering, with proficiency in Python or

Qualifications

• Pursuing degree in Mathematics, Statistics, Finance, Engineering, or related field.
• 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.

Responsibilities

• Implement regression and forecasting models using Python or R.
• Explore time-series analysis and econometric techniques.
• Conduct Monte Carlo simulations and basic optimization analyses.
• Visualize results with standard plotting libraries.
• Summarize model performance, assumptions, and limitations in reports.

Skills

Statistics

Time-series analysis

Communication

Education

Bachelor's student in Mathematics/Statistics/Finance

Tools

Python

R

Pandas

Matplotlib

Job description

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