Marketing Mixed Modelling Data Scientist (London Area)

Data Scientist with PyMc & Marketing Mixed Modelling experience

6 Months Contract

Inside IR35

Remote/1 day onsite a month

Bankside

My client a top Global company are currently looking to recruit a Data Scientist with PyMc & MMM experience to join their team on a 6-month contract basis. Please note if successful this position will need to set up via an Umbrella Company/PAYE. This Senior Data scientist required to work with our clients Data Science team to drive Marketing Effectiveness using Marketing Mix Modelling, Multi-Touch Attribution, and other models.

Responsibilities:

• 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:

• Proven experience in developing and implementing Marketing Mix Models
• Be an expert in PyMc, Python and familiar with R programming for MMM Models
• Have in depth understanding of statistical modelling / ML techniques
• 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)