Senior Applied Scientist, Japan Prime & Marketing

The Japan Prime & Marketing team drives customer growth and engagement for Amazon Japan. Our applied science team combine advanced machine learning with deep business understanding to deliver experiences that delight customers and grow the Prime membership base in one of Amazon's most dynamic and competitive marketplaces.

We are seeking a Senior Applied Scientist to lead the science for personalization and customer growth initiatives across Japan Points, promotional campaigns, and Prime membership engagement. You will own end-to-end science solutions — from problem formulation and data analysis through model development, A/B testing, and production deployment — that directly impact millions of Japanese customers.

This is a high-visibility role where you will define the science roadmap, influence business strategy with data-driven insights, and collaborate with product, engineering, economics, and marketing teams across Japan and globally.

At Amazon, you'll work alongside the latest AI and GenAI tools that are increasingly woven into how teams operate: from AI-powered capabilities that accelerate decision-making, to Generative AI that helps you focus on work that truly matters. You'll have opportunities and resources to develop AI fluency at your own pace, with continuous learning built into the culture.

• Key job responsibilities
• - Define and execute the science roadmap for personalization, points optimization, promotions targeting, and customer growth within Japan Prime & Marketing
• - Design and develop machine learning models for customer segmentation, lifetime value prediction, churn propensity, and next-best-action recommendation to drive Prime acquisition and retention
• - Build optimization frameworks for Japan Points allocation, promotional offer targeting, and budget efficiency that maximize long-term customer value rather than short-term engagement
• - Apply causal inference, experimentation design, and econometric methods to measure the incremental impact of points, promotions, and marketing interventions
• - Develop personalization systems that tailor offers, messaging, and incentive structures to individual customer preferences and lifecycle stages
• - Lead the design and analysis of large-scale A/B tests and quasi-experimental studies to validate model performance and business impact
• - Collaborate with engineering teams to integrate models into production systems with millisecond-level latency requirements serving millions of daily active customers
• - Influence senior leadership through clear communication of scientific findings, trade-offs, and strategic recommendations
• - Mentor junior scientists and raise the scientific bar across the team through code reviews, design reviews, and knowledge sharing
• - Contribute to the broader scientific community through internal and external publications at peer-reviewed venues

Basic qualifications

• - 3+ years of building machine learning models for business application experience
• - PhD, or Master's degree and 6+ years of applied research experience
• - Experience programming in Java, C++, Python or related language
• - Experience with neural deep learning methods and machine learning
• - Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability

Preferred qualifications

• - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• - Experience with large scale distributed systems such as Hadoop, Spark etc.
• - Have publications at top-tier peer-reviewed conferences or journals
• - Experience with promotional strategy, loyalty programs, or pricing science
• - Experience with causal inference, experimentation, or econometric methods

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