CORSAIR is looking to solve e-commerce performance metrics analysis, customer segmentation, and AI-driven use cases such as personalization, merchandising intelligence, and content optimization
Requirements
- Strong proficiency in Python for data analysis
- Solid understanding of statistics, experimentation, and basic machine learning concepts
- Experience working with structured datasets using tools such as Pandas and NumPy
- Familiarity with AI or ML frameworks such as scikit-learn, PyTorch, or TensorFlow
- Exposure to content management or AI content tools (e.g., Contentful, Jasper, Omnify)
- Exposure to Model Context Protocols (MCP) or context-aware, agent-based workflows
- Understanding of experimentation platforms or conversion optimization frameworks
Responsibilities
- Perform customer, product, and behavioral segmentation analysis using transactional, browsing, and content data
- Analyze e-commerce performance metrics and experiment results to identify trends, opportunities, and optimization levers
- Support AI-driven use cases such as personalization, merchandising intelligence, and content optimization
- Design and analyze A/B tests to evaluate changes in content, merchandising, or customer experience
- Work with e-commerce and content platforms such as Contentful, Jasper, and Omnify to understand data structures, workflows, and optimization opportunities
- Assist in building and maintaining data analysis pipelines and exploratory models using Python
- Support MCP-aware workflows where analytics, models, or tools exchange contextual data across systems
Other
- Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Statistics, Economics, Business Analytics, or a related field
- Able to work 40 hours a week onsite throughout the course of the 12-week summer internship
- Legally authorized to work in the United States
- You are actively enrolled as a full-time student in an accredited institution and have completed at least one year of education
- Ability to communicate findings clearly to both technical and non-technical audiences
