Machine Learning Scientist/Applied Scientist, Amazon

• Are you interested in building the measurement foundation that proves whether targeted, cohort-based marketing actually changes customer behavior at Amazon scale?
• 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++

Desirable

• We are seeking an Applied Scientist to own measurement and experimentation for our Lifecycle Marketing Experimentation roadmap within the PRIMAS (Prime & Marketing Analytics and Science) team
• In this role, you will design and execute rigorous experiments that measure the effectiveness of audience-based marketing campaigns across multiple channels, providing the evidence that guides marketing strategy and investment decisions
• This is a high-impact role where you will build measurement frameworks from scratch, design experiments that isolate causal effects, and establish the experimental standards for lifecycle marketing across EU
• You will work closely with business leaders and the senior science lead to answer critical questions: does targeting specific cohorts (Bargain hunters, Young adults) improve efficiency vs. broad campaigns? Which creative strategies drive behavior change? How should we optimize marketing spend across channels?
• Own measurement end-to-end for lifecycle marketing campaigns – design experiments (RCTs, geo-tests, audience holdouts) that measure campaign effectiveness across marketing channels
• Build measurement frameworks and experimental best practices that work across different activation platforms and can scale to multiple campaigns
• Establish experimental standards and tooling for lifecycle marketing, ensuring statistical rigor while balancing business constraints
• Apply causal inference methods to measure incremental impact of marketing campaigns vs. counterfactual
• Navigate measurement challenges across different platforms (Meta attribution, LiveRamp, clean rooms, onsite tracking)
• Analyze experiment results and provide optimization recommendations based on statistical evidence
• Establish guardrails and success criteria for campaign evaluation
• The PRIMAS team, is part of a larger tech tech team of 100+ people called WIMSI (WW Integrated Marketing Systems and Intelligence). WIMSI core mission is to accelerate marketing technology capabilities that enable de-averaged customer experiences across the marketing funnel: awareness, consideration, and conversion