Senior Data Scientist (Econometrician / Marketing Mix Modelling Specialist) Xcede are currently partnered with a leading Consumer Healthcare company who are in the process of develivering a Global Marketing Effectiveness program using Marketing Mix Modelling, Multi-Touch Attribution, and other models. They are seeking a Senior Data Scientist with a minimum of 1 years experience focused on Marketing Mix Modelling (MMM), and a strong background, 7+ years on Statistical and Regression based modelling. Responsibilities will include: • Oversee and be responsible for data collection including data extraction and manipulation, data analysis and validation. • Analyse all datasets to ensure that each KPI is understood, and data is ready for modelling. • Proficiency in using Excel/SQL/Python/Pandas to process, transform, create variables, and build models. • Build base models according to the project specification, incorporating all drivers of KPIs, providing rationale for variables selection, understanding coefficients and contributions. • Taking base models, oversee or build in additional improvements and progress the model towards finalisation • Create sales effect/ ROI workbook, • Create response curves and optimisation charts • budget allocation. Run scenarios required to answer client objectives for the purpose of forward looking optimization, • Validate models, identify areas of weakness, suggest and test possible improvements and ensure robustness and validity. Requirements: • Minimum 5 years of proven experience in developing and implementing Marketing Mix Models • Be an expert in Python and familiar with R programming for MMM Models • Have in depth understanding of statistical modelling / ML techniques • Strong experience with Regression based models applied to the context of MMM modelling • Solid experience with Probabilistic Programming and Bayesian Methods • Be an expert in mining large & very complex data sets using SQL and Spark • Have in depth understanding of statistical modelling techniques and their mathematical foundations, • Have a good working knowledge of Pymc and cloud-based data science frameworks and toolkits. Working knowledge of Azure is preferred • Have a deep knowledge of a sufficiently broad area of technical specialism (Optimisation, Applied Mathematics, Simulation) • MS or PhD degree in Data Science, Computer science, applied mathematics, statistics, or another relevant discipline with a strong foundation in modelling and computer science.