Description: Advertisers need to compare media activation strategies (e.g., Always-On vs On/Off) but often lack granular plans, making scenario construction slow and manual. This project converts high-level strategies into detailed, week-level laydowns compatible with forecasting tools, automating scenario generation to enable faster comparisons and clearer decision-making.

Key attributes / Main competencies:

• Experience with data science toolkits (Python, R, etc.)
• Understanding of econometrics concepts and MMM methodologies
• Familiarity with data processing techniques and best practices
• Proficiency in Python
• Strong analytical and problem-solving skills

Learning outcomes:

• Develop a methodology to translate strategies into granular MMM-ready scenarios
• Automate scenario creation to enhance forecasting speed and reliability
• Improve consistency and scalability of laydown planning
• Deepen understanding of media planning logic and MMM forecasting workflows