Machine Learning Scientist / Applied Scientist, EU Prime and Marketing Analytics & Science (primas)

Amazon Amazon · Big Tech · B, Spain +1 · Applied Science

This role focuses on building measurement and experimentation frameworks for lifecycle marketing campaigns within Amazon's EU Prime and Marketing Analytics & Science (PRIMAS) team. The scientist will design and execute experiments to measure campaign effectiveness, establish experimental standards, and apply causal inference methods to guide marketing strategy and investment decisions. The role involves working with various marketing channels and platforms, analyzing results, and providing optimization recommendations.

What you'd actually do

  1. Own measurement end-to-end for lifecycle marketing campaigns – design experiments (RCTs, geo-tests, audience holdouts) that measure campaign effectiveness across marketing channels
  2. Build measurement frameworks and experimental best practices that work across different activation platforms and can scale to multiple campaigns
  3. Establish experimental standards and tooling for lifecycle marketing, ensuring statistical rigor while balancing business constraints
  4. Apply causal inference methods to measure incremental impact of marketing campaigns vs. counterfactual
  5. Navigate measurement challenges across different platforms (Meta attribution, LiveRamp, clean rooms, onsite tracking)

Skills

Required

  • PhD in computer science, machine learning, engineering, or related fields
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
  • Experience building machine learning models or developing algorithms for business application
  • Experience with programming languages such as Python, Java, C++

Nice to have

  • Experience in professional software development
  • Experience in designing experiments and statistical analysis of results

What the JD emphasized

  • build measurement frameworks from scratch
  • design experiments that isolate causal effects
  • establish the experimental standards for lifecycle marketing

Other signals

  • measurement and experimentation for marketing campaigns
  • design and execute rigorous experiments
  • measure the effectiveness of audience-based marketing campaigns
  • build measurement frameworks from scratch
  • design experiments that isolate causal effects
  • establish the experimental standards for lifecycle marketing