Data Scientist

Experis is partnering with an e-commerce business based in Hamburg to appoint a Data Scientist – Marketing. The Data Scientist – Marketing will play a key role in analysing, measuring and ultimately optimising performance across marketing investments.

This is an exciting opportunity for a proven Data Scientist with hands on Marketing Mix Modelling (MMM), attribution modelling, and marketing analytics expertise to provide insights that optimise decision-making, spend, and drive growth.

The Role:

• Working with Marketing Mix Models to understand impact across various marketing channels
• Multi-touch attribution frameworks – including probabilistic, rule-based, algorithmic
• Partnering with key stakeholders and external vendors
• Guiding media planning and allocation of budget through the translation of performance data into investment insights
• Building dashboards & reports
• Monitoring and defining customer and marketing KPIs
• Collaborating with Finance, Performance Marketing, CRM, and Analytics Engineering - ensuring high levels of data quality, accuracy in models, and scalable implementation
• Play a key part in growing the analytics maturity of the organisation

The Requirements:

• Experience gained in an ecommerce business
• Track record and expertise in marketing analytics
• Expertise in Marketing Mix Modelling (MMM), as well as attribution, and marketing performance optimisation (specific to customer acquisition)
• Expertise in statistical modelling (such as Bayesian methods and regression), causal inference, as well as experimentation design
• Advanced SQL
• Proficiency with Python or R
• Experience with a modern analytics stack (for example, Google Analytics, Redshift, Tableau etc.)
• Have worked across a variety of marketing channels (whether that’s social, search, CRM, TV etc.)
• Excellent communication skills, with the ability to distil the complex into the simple for non-technical stakeholders
• Excellent stakeholder engagement skills at all levels
• Experience working with measurement providers who are external, as well as internal engineering and data science teams
• Collaborative, proactive, and hold a passion for translating data into value led decision making