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.