Amazon Japan is seeking a Data Scientist to join our Cost-to-Serve Intelligence team — a group that answers the question: "Why does it cost what it costs to deliver a package, and how do we do it more efficiently?" You will design and run research studies that connect operational data to business decisions, helping leadership understand where to invest to reduce cost-to-serve across Japan's logistics network — ultimately enabling faster, cheaper delivery that improves the customer experience. 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. This is a science role with direct business impact. Your work will be presented to senior executives, sized in dollar terms, and used to prioritize multi-million-dollar operational investments. The cost savings you identify flow back to customers through lower prices and faster delivery. If you enjoy turning complex data into clear recommendations that people act on, this is the role. Key job responsibilities - Design and execute quantitative studies that explain why cost-to-serve moves — isolating root causes from noise and quantifying improvement opportunities - Bridge science to business decisions: translate statistical findings into investment recommendations, opportunity sizing, and initiative prioritization that leadership can act on - Partner cross-functionally with operations, finance, supply chain, and product teams to define research questions, validate findings, and ensure insights drive real-world action - Own the full research lifecycle — from problem framing and data exploration through methodology design, analysis, and stakeholder-ready deliverables - Apply a range of scientific methods (econometrics, statistical modeling, machine learning, AI-assisted analysis) matched to the problem at hand - Communicate findings effectively to both technical and non-technical audiences through structured documents, presentations, and data visualizations - Continuously improve the team's analytical toolkit — introducing new methods, automating repetitive analysis, and raising the bar on scientific rigor A day in the life You might start the morning in a sync with your Applied Scientist partner, reviewing outputs from a model that estimates how different operational levers impact cost-to-serve. Mid-morning, you join a working session with a partner team in supply chain or finance, aligning on what questions your next study should answer and what data you'll need. After lunch, you're building and validating a quantitative model — using Python, SQL, and AI-powered tools to test causal hypotheses, estimate coefficients, and ensure the model delivers reliable insights at scale. You then structure your findings into an actionable recommendation — quantifying the opportunity and proposing where to double down. You close the day preparing materials for a leadership review, translating your model outputs into a narrative that drives decisions. Your stakeholders span supply chain, operations, finance, and product. You are the person leaders come to when they need to understand why something changed, how much it matters, and what to do next. About the team We are a multi-disciplinary team of ~10 people — data scientists, applied scientists, product managers, and data engineers — who own Cost-to-Serve intelligence end-to-end: from the business questions through product strategy to the technical systems that deliver answers. We sit within JP Consumer Innovation and operate at the intersection of multiple organizations (operations, finance, supply chain, technology), giving us broad visibility and outsized influence on Amazon Japan's P&L. The work is high-visibility and high-impact. Our insights and products are consumed by VP-level executives and directly shape Japan-wide investment priorities. When we find a way to reduce cost-to-serve, that efficiency flows through to customers as faster delivery and better prices — the virtuous cycle at the heart of Amazon's flywheel. The culture is intellectually rigorous but collaborative — we publish internal science papers, present at company-wide summits, and run cross-functional knowledge-sharing sessions with hundreds of attendees. We value clear thinking over title, and mechanism over assertion. We are based in Tokyo and operate bilingually (English/Japanese).
