Marketing Science Partner - Mid Market EMEA 6-Month STE Contract

We unlock the full potential of businesses on Meta platforms by transforming advertising performance and industry practices through the use of data-driven analytical methods.

We are specifically looking for a candidate with deep analytical, critical thinking skills and a passion for generating data-driven insights. This role will challenge you to not only provide strategic direction to advertisers about their measurement efforts, but also to create tools, develop programmes and identify ways to automate Marketing Science guidance across thousands of advertisers.

• 8+ years of experience working in marketing effectiveness, market research, or business intelligence
• Ability to explain complex analytical concepts and/or problems in a simple way
• Proven experience with creating and delivering analytics-based presentations to decision makers
• Experience in analyzing and manipulating data sets to understand patterns and provide insights
• Understanding of test design methodologies (geo-tests, A/B tests, multivariate tests, Randomised Control Trial experiments) along with survey sampling, and/or panel data
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience in operating scaled programs
• Resourceful & process orientated
• Experience in complex project and process management
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Master's degree in an analytical field such as computer science, engineering, mathematics, statistics, economics
• Familiarity with basic commands in R and/or Python
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Familiarity with SQL
• Understanding of the advertising landscape
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)