Marketing Data Scientist (m/f)

优缺点分析

角色解读

• Potential progression to senior data scientist or lead analytics roles within Henkel's global analytics network.
• Opportunity to specialize deeper in marketing analytics or expand into broader data science applications.
• International mobility and cross-functional exposure due to Henkel's global presence.
• Develop and maintain Marketing Mix Models (MMM) using Python and Google Meridian library.
• Collect, validate, and structure data from internal and external sources to support model inputs.
• Generate actionable insights and recommendations for optimizing marketing budget allocation.
• Collaborate with international stakeholders to present findings and support data-driven decisions.
• Advanced Python programming, especially with Pandas, PyMC, and Meridian.
• Solid statistical knowledge including regression analysis and model interpretation.
• Proficiency in MS Excel for data manipulation and reporting.
• Strong communication skills in English for presenting complex results to non-technical stakeholders.

申请策略

• Tailor your CV to emphasize quantitative marketing analytics, not just general data science.
• Research Henkel's brands and recent marketing strategies to show genuine interest.
• Highlight any experience with marketing mix modeling, attribution, or econometric analysis.
• Showcase practical Python projects, especially using Pandas, PyMC, or similar libraries.
• Emphasize your ability to translate analytical results into business recommendations.
• Mention experience collaborating with marketing or business stakeholders.
• Familiarize yourself with Google Meridian and Bayesian modeling approaches (PyMC).
• Practice MMM concepts: media saturation, diminishing returns, and budget optimization.

面试指南

• Use the STAR method for behavioral questions: Situation, Task, Action, Result.
• For technical questions, outline a structured approach: data collection, data cleaning, model selection, validation, and interpretation.
• In all answers, emphasize business impact and decision-making outcomes.
• How would you build a Marketing Mix Model from scratch? What challenges might you face?
• Explain how you handle missing or inconsistent data in a marketing dataset.
• Describe a time you presented complex analytical results to a non-technical audience.
• What is your experience with Bayesian methods or probabilistic programming in Python?
• How would you use MMM outputs to recommend budget reallocation across channels?