Marketing Data Scientist / Econometrician

Marketing Data Scientist / Econometrician Location: London (Hybrid Working) Contract Type: Initial 12 month contract + potential to extend long-term due to 3/4 year project scope. Start Date: ASAP We have an excellent opportunity for a Marketing Data Scientist / Econometrician to join a leading FMCG Brand, Initial 12 month contract + opportunity to extend long-term. This is an exciting opportunity to join a high-performing Data Science team focused on advancing marketing effectiveness through advanced econometric modelling—including Bayesian Marketing Mix Modelling (MMM), Multi-Touch Attribution (MTA), and data-driven optimization strategies. Key Responsibilities • Lead and manage data workflows: data extraction, transformation, validation, and exploratory analysis to ensure modelling-readiness. • Build and refine Bayesian MMM models that capture the drivers of key marketing and commercial KPIs. • Use Python (and optionally R) to design, build, and improve base and advanced models—integrating prior knowledge, probabilistic reasoning, and real-world constraints. • Develop and present ROI workbooks, response curves, and optimization frameworks for marketing budget allocation. • Run scenario-based simulations to support strategic planning and forward-looking marketing investment decisions. • Validate and stress-test models, identifying opportunities for improvement and ensuring robustness, interpretability, and business relevance. Requirements • Extensive experience in building and deploying Marketing Mix Models, with a strong focus on Bayesian methods. • Expert-level proficiency in Python, especially with pandas, NumPy, and probabilistic programming libraries such as PyMC. • Experience with R is a bonus, particularly for MMM-related workflows. • Deep understanding of regression modelling, Bayesian inference, hierarchical models, and MCMC techniques. • Proven ability to handle and analyse large, complex datasets using SQL and/or Spark. • Solid knowledge of applied statistics, modelling techniques, and the mathematical underpinnings of inference and simulation. • Familiarity with cloud platforms (Azure preferred) and modern data science toolkits. • Advanced degree (MSc or PhD) in Statistics, Data Science, Applied Mathematics, Computer Science, or a related quantitative field. Preferred Attributes • Strong foundation in optimization, simulation modelling, and decision analytics. • Demonstrated ability to translate complex Bayesian models into strategic insights and practical business outcomes. • Strong communication skills and the ability to collaborate across marketing, analytics, and commercial teams.