Machine Learning Scientist / Applied Scientist, EU Prime and Marketing Analytics & Science (PRIMAS)
Core
Design and run experiments to determine if adding Amazon's 1P audience signals improves marketing efficiency over Value-Based Optimization (VBO) for EU campaigns.
Role type
Applied Scientist (Marketing Analytics & Science)
Builds
1P audience segments, experimental infrastructure, and measurement frameworks for Meta, Google, and Amazon Display Ads.
Domain
E-commerce marketing, causal inference, and audience targeting
Deliverable
production ML models
Required skills
causal inference, experimental design, algorithms and data structures, numerical optimization, machine learning model development, Python, Java, C++, statistical analysis
Preferred skills
professional software development, designing experiments, solving business problems with ML
Technologies
Python, Java, C++
Rewrite
## Responsibilities
- Build and validate 1P audience segments from Amazon behavioral, transactional, and lifecycle data
- Design experiments that isolate the incremental effect of 1P audience signal over platform VBO baselines
- Deploy audiences across activation surfaces and establish measurement standards that make cross-surface comparison valid
- Apply causal inference methods to measure the true incremental lift of audience-based targeting vs. VBO
- Develop power analysis frameworks and guardrails that enable rapid experimentation without underpowered or conflated tests
- Deliver optimization recommendations grounded in experimental evidence: which cohorts respond, which surfaces deliver, which creative strategies drive behavior change
- Build reusable audience and measurement frameworks that can be deployed across campaigns and channels — year 1 experiments should produce infrastructure, not one-off analyses
- Document experimental learnings in a way that informs both the 2026 roadmap and the business case for investing further in 1P audience capabilities in 2027+
- Partner with engineering and PMT to translate validated audience prototypes into production-ready solutions that scale beyond the experimentation phase
## Requirements
- PhD in computer science, machine learning, robotics, statistics, mathematics, operations research, engineering, or equivalent quantitative field
- 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
- Experience in solving business problems through machine learning, data mining and statistical algorithms
## Benefits
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
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