Marketing Analytics Analyst – Market Mix Modeling

Role Overview We are hiring a hands-on Marketing Analytics Analyst with 34 years of experience to work on a greenfield Market Mix Modeling (MMM) and marketing effectiveness initiative. This is a build role, not a tool-operation or reporting role. The analyst will design, develop, and implement MMM and marketing analytics solutions from raw data using Python and SQL, without reliance on proprietary or pre-built MMM platforms. The role is best suited for professionals with exposure to FMCG / Consumer / Marketing Analytics who are comfortable working in unstructured problem environments and can take ownership of data, modeling, and insights end-to-end. Key Responsibilities Market Mix & Marketing Analytics (Core) 1. Build and support Market Mix Models (MMM) from raw sales, media, promotion, and calendar data 2. Apply regression and time-series techniques, including lag/decay (adstock) and saturation effects 3. Evaluate model stability, interpret results, and derive actionable marketing insights 4. Support marketing ROI measurement, spend optimization, and planning decisions Data Engineering & Analytics (Hands-On) 1. Write complex SQL queries to extract, join, validate, and transform data from multiple source tables 2. Use Python for data cleaning, feature engineering, statistical analysis, and automation 3. Build analytical base datasets for modeling from raw sales, media, and promotion data 4. Independently handle data quality issues, missing values, and outliers 5.Automate recurring analytical workflows and ensure reproducibility Business & Stakeholder Engagement 1. Partner with Marketing, Commercial, and Analytics stakeholders to define KPIs and measurement frameworks 2. Convert analytical outputs into clear business narratives, recommendations, and presentations 3. Support decision-making related to marketing ROI, spend optimization, and planning Required Qualifications 1. Bachelors degree in Statistics, Economics, Mathematics, Engineering, Data Science, or a related quantitative discipline 2. 3-4 years of hands-on experience in Marketing Analytics, Commercial Analytics, or Business Analytics 3. Strong hands-on proficiency in Python for data preparation, statistical analysis, and modeling 4. Strong hands-on proficiency in SQL, including complex joins, aggregations, and analytical queries 5. Experience working with raw, multi-source datasets and building analytical solutions from scratch 6. Solid understanding of regression and time-series concepts used in marketing or commercial analytics 7. Ability to translate analytical outputs into clear, actionable business insights