Job
Description
Key Responsibilities: Develop and implement Media Mix Models to optimize marketing spend across different channels (e.g., TV, digital, radio, print, etc.). Analyze historical data to understand the impact of marketing efforts and determine the effectiveness of different media channels. Collaborate with marketing and business teams to translate business objectives into quantitative analyses and actionable insights. Build predictive models to forecast the impact of future marketing activities and recommend budget allocation. Present and communicate complex findings in a clear, concise, and actionable manner to both technical and non-technical stakeholders. Perform deep-dive analyses of marketing campaigns and customer data to identify trends, opportunities, and areas for improvement. Ensure data integrity, accuracy, and consistency in all analyses and models. Stay up-to-date with the latest trends and advancements in media mix modeling, marketing analytics, and data science. Collaborate with cross-functional teams including Data Engineering, Marketing, and Business Intelligence to ensure seamless data flow and integration. Create and maintain documentation for all models, methodologies, and analysis processes. Qualifications Bachelors or Masters degree in Data Science, Statistics, Economics, Mathematics, or a related field. Proven experience (6+ years) working in Media Mix Modeling (MMM) and/or marketing analytics. Strong proficiency in statistical modeling techniques (e.g., regression analysis, time-series modeling) and data analysis. Hands-on experience with tools and technologies such as Python, R, SQL, and data visualization platforms (e.g., Tableau, Power BI). Familiarity with marketing data sources (e.g., Nielsen, IRI, social media data, CRM, etc.). Excellent problem-solving skills and a strong analytical mindset. Ability to translate complex data into actionable insights and recommendations for business stakeholders. Strong communication skills with the ability to present findings to both technical and non-technical audiences. Experience working in a fast-paced, data-driven environment. Familiarity with machine learning techniques and frameworks is a plus. Preferred Qualifications: Experience working with large datasets and cloud-based data platforms (e.g., AWS, Azure, Google Cloud). Knowledge of marketing attribution models, customer segmentation, and lifetime value (LTV) analysis. Experience in running A/B tests and controlled experiments. Prior experience in a consulting or marketing agency environment is a plus.
