We are seeking an exceptional Applied Scientist to join our JP Seller Services team, where you will reimagine how science analysis and modeling are conducted across the organization through an AI-native approach. In this role, you will design and build intelligent systems that enable any team member to validate business hypotheses with scientific rigor in hours rather than months. You will architect production-grade platforms spanning multi-agent AI frameworks, causal inference automation, generative AI, and simulation engines that democratize advanced analytics at scale. Your work will fundamentally transform how the teams generate, test, and deploy data-driven recommendations, scaling rigorous science solutions for every decision-maker to solve customer problems. The ideal candidate combines deep expertise in scientific analysis such as causal inference, machine learning, and AI system design with the vision to rethink the entire science lifecycle from hypothesis to deployment.
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
- Lead the design and development of AI-native science platforms that automate the end-to-end lifecycle from hypothesis formulation through causal analysis, model validation, and deployment into production systems.
- Design and build shared knowledge infrastructure (feature stores, experiment registries, model leaderboards) that enables cumulative organizational learning, where every validated insight accelerates future analyses.
- Design and implement evaluation frameworks, including Seller simulations, that enable teams to validate model quality and test interventions against synthetic populations before live deployment.
- Drive integration with downstream systems to close the gap between validated insights and seller-facing actions, ensuring science outputs reach the people and systems that serve customers.
- Collaborate with cross-functional partners (product managers, category leaders, marketing managers, economists, and data scientists) to identify high-impact business problems and translate them into scalable scientific solutions.
Basic Qualifications
- PhD, or Master's degree and 5+ years of building machine learning models for business application experience
- Knowledge of programming languages such as C/C++, Python, Java or Perl
Preferred Qualifications
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
- Speak, write, and read fluently in Japanese at a business level or above (N1+)
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