Description: This project tackles multicollinearity in Marketing Mix Modeling (MMM), where simultaneously activated media channels create correlated variables that destabilize models, inflate coefficients, and reduce interpretability. The goal is to explore causes, evaluate industry techniques, and improve/automate MASS Analytics’ methodology for scalable, transparent composite-variable creation aligned with marketing logic.

Key attributes / Main competencies:

• Experience with common data science toolkits (Python, R, etc.)
• Good understanding of econometrics concepts and modeling methodologies
• Familiarity with data processing techniques and best practices
• Strong Python skills
• Analytical and problem-solving mindset

Learning outcomes:

• Develop a structured understanding of multicollinearity and its impact on MMM
• Identify, assess, and automate key steps in composite-variable creation
• Improve modeling efficiency and reliability through better variable engineering
• Build a scalable app/framework to automate creation of transformed variables with clear business logic