Careers

Come be a part of a rapidly expanding $35 billion dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. The Opportunity This is one of the most consequential science leadership roles in Amazon Business. As Applied Science Manager, you will directly influence over $40B in annual revenue by owning the science strategy that powers how we acquire, engage, monetize, and retain millions of business customers worldwide. Your models won't sit in notebooks. They will run in production, shaping real-time decisions across the full customer lifecycle for one of Amazon's fastest-growing businesses. You will operate at the intersection of Business Prime, Marketing Science, and Customer Acquisition three of the highest-visibility, highest-impact charters in the organization. Your work will be presented to VPs and SVPs, inform multi-billion-dollar investment decisions, and fundamentally reshape how Amazon Business goes to market. This is not an incremental optimization role. This is a build-the-future role where your science roadmap becomes the business strategy. What You'll Own Team Leadership & Talent Development: Build and lead a world-class team of applied scientists and research scientists. Set the technical vision, define the multi-year science roadmap, and create an environment where scientists ship models that move the needle on $40B+ in revenue. You will be the bar-raiser who attracts top talent and develops the next generation of science leaders at Amazon. Customer Lifecycle Intelligence ($40B+ Revenue Impact): Own the predictive modeling stack that powers customer identification, targeting, spend behavior forecasting, churn propensity, and declining engagement detection across every Business Prime segment. Your models will determine which customers we invest in, how we invest, and when directly impacting billions in customer lifetime value. Marketing Science & Measurement (Cross-Functional, Multi-Billion Dollar Allocation): Build the causal inference and attribution frameworks that determine how Amazon Business allocates hundreds of millions in marketing investment across paid, owned, and earned channels globally. You will be the single point of science truth for incrementality measurement, marketing mix modeling, and ROI optimization partnering with Marketing, Finance, CPS, and international teams to ensure every dollar drives maximum impact. Customer Acquisition Engine: Lead the science that accelerates new Business Prime member acquisition at scale. Build propensity models, audience optimization systems, and channel effectiveness frameworks that feed directly into acquisition engines serving 10+ global markets. Your work will be the difference between linear growth and exponential growth. Benefits Adoption & the "Aha Moment": Build behavioral segmentation and recommendation systems that identify, for each customer vertical and organizational profile, the exact moment and feature combination that converts a trialist into a loyalist. This is the science of delight at scale. Trust & Safety at Scale: Develop fraud detection models, abuse identification systems, and eligibility classifiers that protect the integrity of the Business Prime ecosystem safeguarding billions in revenue from bad actors. Experimentation & Causal Inference (Organization-Wide): Establish the experimentation frameworks and causal inference methodologies used across the entire Amazon Business organization. Design experiments that measure incremental impact of product launches, pricing changes, and marketing interventions creating the scientific foundation for how leadership makes high-stakes decisions. Why This Role Is Different This is not a role where you optimize a feature in isolation. You will sit at the center of a complex, cross-functional operating model partnering daily with 7+ senior leaders (including Directors and VPs) across Business Prime, CPS Sales, Central Marketing, SSR, GTMO, Finance, and International teams. Your science will be the connective tissue that aligns these organizations around a shared, data-driven growth strategy. You will have direct senior leadership visibility — presenting findings, recommendations, and roadmaps to VP audiences regularly. The insights your team generates will shape OP1/OP2 planning, Board-level narratives, and multi-year investment theses. What We're Looking For The successful Applied Science Manager will have an bias for action in a startup environment, with people leadership skills, a proven ability to build and manage high-performing science teams, define and prioritize research agendas that map to business outcomes, and build methodology and tools that are statistically grounded. You influence product and business strategy through science translating complex analytical findings into crisp, actionable recommendations that senior leaders act on immediately. We are seeking someone who thrives in a fast-paced, high-energy, and fun work environment where we deliver value incrementally and frequently. You are a highly technical leader who knows your subject matter deeply, can elevate the technical bar of your team, and is energized by ambiguity and new problem spaces. You know how to deliver results and show a desire to develop yourself, your colleagues, and your career.

Our organization in Amazon Robotics builds robots that perform contact-rich manipulation safely and reliably in complex, unstructured environments, at Amazon scale. Our scientists and engineers push the boundaries of robotic manipulation to handle enormous object diversity, bringing deep expertise across planning, control, perception, and machine learning. We learn from real-world data at a scale that few teams in robotics can access. We are seeking an experienced Senior Applied Scientist to help guide a small team advancing reinforcement learning for manipulation. We are creating robots that learn how to push, flip, rearrange, and dexterously insert items with unparalleled robustness, speed, and reliability. Our goal is to deploy robots that will work across Amazon's global network and can handle the full diversity of items that Amazon sells. You will set the technical direction for how we learn these behaviors, from simulation training through reliable execution on physical robots, and you will demonstrate new manipulation capabilities on real hardware at scale. This team's mission reaches beyond any single product: to invent and apply manipulation capabilities that generalize to many future robotics applications. The robots our organization already deploys at scale give you a rare proving ground to collect data, run experiments, and get new policies onto real hardware faster than almost anywhere in the field. You will raise the bar for scientific rigor and engineering quality, and mentor other scientists as the team grows. Key job responsibilities - Set the technical direction for learning non-prehensile and contact-rich manipulation policies, from testing the latest advances in the field through demonstrated capability on hardware. - Oversee the development of reinforcement learning approaches that address the long tail of diverse, demanding manipulation conditions. - Own the path from simulation training to reliable, real-time execution on physical robots, making evidence based calls on where learned approaches should replace engineered ones. - Demonstrate new manipulation capabilities on real robots at scale, and turn one-off results into repeatable methods. - Establish the standards, evaluation practices, and data-informed improvement loops that the team builds on. - Mentor scientists and engineers, and raise the bar for applied science rigor and engineering quality. - Partner across control, perception, and hardware to integrate learned behaviors into working systems. - Represent Amazon in academia through publications and scientific presentations. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply

Are you passionate about leveraging data and economics to enhance customer experience across Amazon's diverse businesses? The Customer Experience and Business Trends (CXBT) organization is seeking an Economist to join our Benchmarking Economics Analytics and Measurement (BEAM) team. Our mission is to drive customer experience improvements through innovative economic modeling, advanced analytics, and scalable scientific solutions. As an Economist on our team, you will collaborate with senior management, business stakeholders, scientists, engineers, and economics leadership to solve complex business challenges across Amazon's business lines. You'll develop sophisticated econometric models using our world-class data systems, applying diverse methodologies spanning causal inference, machine learning, and generative AI. In this fast-paced environment, you'll tackle challenging problems that directly influence strategic decision-making and drive measurable business impact. Key job responsibilities - Develop economic theory and deliver causal machine learning models at scale - Collaborate with cross-functional teams to translate research into scalable solutions - Write effective business narratives and scientific papers to communicate to both business and technical audiences - Drive data-driven decision making to improve customer experience About the team Customer Experience and Business Trends (CXBT) is an organization made up of a diverse suite of functions dedicated to deeply understanding and improving customer experience, globally. We are a team of builders that develop products, services, ideas, and various ways of leveraging data to influence product and service offerings – for almost every business at Amazon – for every customer (e.g., consumers, developers, sellers/brands, employees, investors, streamers, gamers). Our approach is based on determining the customer need, along with problem solving, and we work backwards from there. We use technical and non-technical approaches and stay aware of industry and business trends. We are a global team, made up of a diverse set of profiles, skills, and backgrounds – including: Product Managers, Software Developers, Computer Vision experts, Solutions Architects, Data Scientists, Business Intelligence Engineers, Business Analysts, Risk Managers, and more.

We are seeking an Applied Scientist to build production machine learning systems that solve complex business problems at scale. You will design, develop, and deploy ML solutions that directly impact millions of users and drive strategic decision-making across the organization. In this role, you will work on challenging problems spanning predictive modeling, natural language processing, recommendation systems, and generative AI applications. You will collaborate with cross-functional teams to translate ambiguous business challenges into rigorous technical solutions. Key job responsibilities - Design and deploy large-scale machine learning systems in production environments - Develop innovative ML solutions using state-of-the-art techniques including deep learning, NLP, and generative AI - Create ML solutions that personalize manager on-boarding and development experiences — identifying individual capability gaps, recommending tailored learning pathways, and measuring development effectiveness across diverse manager populations and contexts - Partner to build causal inference models and experimental frameworks to measure impact - Collaborate with product managers, engineers, and business leaders to define technical roadmaps - Communicate complex technical concepts to diverse audiences, from technical peers to senior leadership About the team The People eXperience and Technology Central Science Team (PXTCS) uses economics, applied science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.

The Amazon Center for Quantum Computing (CQC) team is looking for a passionate, talented, and inventive Research Engineer specializing in hardware design for cryogenic environments. The ideal candidate should have expertise in 3D CAD (SolidWorks), thermal and structural FEA (Ansys/COMSOL), hardware design for cryogenic applications, design for manufacturing, and mechanical engineering principles. The candidate must have demonstrated experience driving designs through full product development cycles (requirements, conceptual design, detailed design, manufacturing, integration, and testing). Candidates must also have a strong background in both cryogenic mechanical engineering theory and implementation. Working effectively within a cross-functional team environment is critical. Key job responsibilities The CQC collaborates across teams and projects to offer state-of-the-art, cost-effective solutions for scaling the signal delivery to quantum processor systems at cryogenic temperatures. Equally important is the ability to scale the thermal performance and improve EMI mitigation of the cryogenic environment. You will work on the following: - High density novel packaging solutions for quantum processor units - Cryogenic mechanical design for novel cryogenic signal conditioning sub-assemblies - Cryogenic mechanical design for signal delivery systems - Simulation-driven designs (shielding, filtering, etc.) to reduce sources of EMI within the qubit environment. - Own end-to-end product development through requirements, design reports, design reviews, assembly/testing documentation, and final delivery A day in the life As you design and implement cryogenic hardware solutions, from requirements definition to deployment, you will also: - Participate in requirements, design, and test reviews and communicate with internal stakeholders - Work cross-functionally to help drive decisions using your unique technical background and skill set - Refine and define standards and processes for operational excellence - Work in a high-paced, startup-like environment where you are provided the resources to innovate quickly About the team The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. Inclusive Team Culture Here at Amazon, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Export Control Requirement Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.

The Central Science Team within Amazon’s People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for an Applied Scientist to build models at the intersection of prediction, causal inference, and optimization. These models are the science foundation for operational workforce planning at Amazon's scale — predicting who stays, who shows up, and suggesting which levers Amazon should use to ensure smooth business operations and the best employee experience. As an Applied Scientist, you will own the forecasting and prediction models. Your forecasts go straight into planning cycles that commit real hiring, so accuracy, calibration, and being able to explain a number to business partners all matter for success. You will work with other scientists and economists on the team, so you will need an interdisciplinary mindset and an eye on how causal inference and forecasting interact. You will also collaborate with engineers to put models into production, so you should be comfortable owning a live system and not only an analysis. Key job responsibilities - Design and develop forecasting and prediction models that feed Amazon's workforce planning and optimization systems. - Build models and algorithms from prototype to production-level systems, and support them once planners depend on them. - Partner with the team's economists to connect forecasts with causal estimates of policy levers. - Extend models to new sites, shifts, and business lines as the platform expands. - Translate ambiguous business problems into modeling approaches, and drive the technical design with planning, engineering, and operations partners. A day in the life - Analyze data to investigate a forecast miss or model performance, and identify improvements - Brainstorm modeling approaches with fellow scientists and economists on the team - Build and backtest new features for your model - Run a simulation or experiment to evaluate your model's performance - Meet with planning, compensation, or finance partners to review requirements, data, design, or other project decisions - Review code changes or a design document from a fellow scientist or engineer - Write and present a document covering method, results, and recommendations

AWS Global Sales drives adoption of the AWS cloud worldwide, enabling customers of all sizes to innovate and expand in the cloud. Our team empowers every customer to grow by providing tailored service, unmatched technology, and support. We dive deep to understand each customer's unique challenges, then craft innovative solutions that accelerate their success. This customer-first approach is how we built the world's most adopted cloud. Join us and help us grow. Are you a customer-obsessed builder passionate about helping enterprise customers achieve their full potential with Generative AI? Do you have deep data science expertise and the technical pre-sales acumen to help customers evaluate, design, and deploy GenAI and ML solutions on AWS? Do you enjoy building impactful AI agents and agentic applications? Join the GenAI/ML Specialist organization as a Senior Generative AI Data Scientist — a senior individual contributor role requiring deep data science expertise, hands-on ML engineering skills, and executive-level customer engagement. The AGS Specialist organization is part of the customer-facing NAMER sales organization, and is responsible for driving revenue and accelerating adoption of cloud and partner services across diverse customer segments. We work backwards from our customers' most complex and business-critical challenges to develop and execute go-to-market plans that transform ideas into scalable, high-impact businesses. AGS NAMER teams include sales specialists and technical solution architects. As part of the team, you will contribute across the full lifecycle of AWS customer initiatives—from shaping new service and solution concepts to accelerating adoption of established offerings. We pride ourselves on thinking big, delivering exceptional customer outcomes, and collaborating seamlessly across AWS as #OneTeam. Role Description In this role, you will be the Subject Matter Expert (SME) for helping NAMER Enterprise customers design and implement Generative AI solutions that leverage Amazon Bedrock. You will translate customer business challenges into data science-driven solutions using AWS. You will define, design, and deploy machine learning models, agentic workflows, and GenAI applications that accelerate adoption of AWS AI/ML services. You will engage with senior engineers, data scientists, product leaders, and executives at strategic enterprise customers to influence technical decisions and provide structured feedback to AWS product teams. You will interact with customers directly to understand their business problems, help and aid them in implementation of generative AI solutions, deliver briefings and deep dive sessions, and guide customers on adoption patterns and best practices for generative AI. You will build prototypes, proof-of-concepts, and explore novel solutions leveraging Amazon Bedrock, Amazon AgentCore, Strands Agents, and open source frameworks such as LangChain, LangGraph, and CrewAI. This position will focus on agentic workflows with Amazon Bedrock, including Strands Agents, Bedrock Agents, and open source agentic frameworks. You must have deep technical experience working with technologies related to large language models including LLM architectures, model evaluation, and fine-tuning techniques. You should be proficient with design, deployment, and evaluation of LLM-powered agents, tools, and orchestration approaches. You will interface with customer data science teams, ML engineering leadership, and C-suite executives to advise on the latest techniques, model architectures, and emerging research. This includes staying current with state-of-the-art approaches from recent publications and research papers — such as advances in reasoning models, multi-agent systems, retrieval-augmented generation, reinforcement learning from human feedback (RLHF), and novel fine-tuning methods — and translating those findings into practical, production-ready solutions for enterprise customers. You will lead technical deep dives and whiteboard sessions with customer chief data scientists and VPs of AI/ML, bridging the gap between research and real-world implementation on AWS. You should understand the security and compliance requirements for ML/GenAI implementations. You should have experience architecting end-to-end ML/GenAI agentic applications for customers using AWS services and the Well-Architected Framework. As the ideal candidate, you bring a deep data science background and the business acumen required to lead complex engagements with large enterprises. You have hands-on expertise in statistical modeling, traditional ML, and current areas such as LLMs, RAG, fine-tuning, AI system evaluation, prompt engineering, agents, and AIOps. You are able to credibly advise senior technical and executive stakeholders on architectural trade-offs, best practices, and risk mitigation. Key job responsibilities - Working with NAMER Enterprise customers' development and data science teams to deeply understand their business and technical needs. Design and implement solutions that make the best use of the AWS cloud platform and AWS AI/ML services including SageMaker, Amazon Bedrock, Amazon AgentCore, and other AI/ML services. - Customer Advisor — Implement and deploy state-of-the-art machine learning and Generative AI solutions. Build prototypes, PoCs, and explore new solutions. Interact closely with enterprise customers to accelerate their AI/ML adoption. - Partner with Data Scientists, SAs, Sales, Business Development, and the AI/ML Service teams to accelerate customer adoption and revenue attainment in NAMER for Amazon Bedrock, SageMaker, and related services that support GenAI use cases. - Thought Leadership – Evangelize AWS GenAI services and share best practices through forums such as AWS blogs, whitepapers, reference architectures, and public-speaking events such as AWS Summit, AWS re:Invent, etc. - Act as a technical liaison between customers and the Amazon Bedrock or other service teams to provide customer-driven product improvement feedback. - Develop and support an AWS internal community of GenAI-related subject matter experts in the AMERICAS. Create field enablement materials for the broader technical population, to help them understand how to integrate AWS GenAI solutions into customer architectures. A day in the life Your day will be dynamic and impactful. You'll engage with enterprise business leaders, dive deep into ML architectures and data science pipelines, and craft transformative AI strategies. You'll collaborate across teams, translating complex statistical and AI concepts into clear, actionable solutions that drive meaningful business outcomes for NAMER Enterprise customers. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.

Are you passionate about data, enjoy solving complex analytical problems, leveraging industry leading agentic AI technologies to derive insight at scale - all in a challenging, fast-paced environment? We are seeking an Applied Scientist to accelerate the growth of Amazon Internal Audit’s Data Science & Risk Intelligence initiatives. The team builds ML and AI solutions that expand self-service data utilization by audit teams, utilizing the right methods to derive deeper patterns, and surface insights to gain holistic perspectives while amplifying potential risk mitigation. Key job responsibilities - Partner with audit teams, product managers, engineers, and scientists to define and deliver machine learning and generative AI products that carry significant ambiguity, scale, and complexity, owning problems end-to-end, from framing through measurable impact. - Design, build, and own agentic AI systems, including multi-agent workflows, retrieval-augmented generation, and tool-using agents, that automate and augment audit work, and set the standard for how the team evaluates them through rigorous LLM-as-judge and human-aligned evaluation. - Apply statistical analysis and classical machine learning using SQL and scripting languages like Python/R over large datasets to develop insights and recommendations that strengthen internal audit. - Architect secure, scalable solutions on AWS machine learning and generative AI services (e.g., Bedrock, AgentCore, SageMaker), owning the full lifecycle from design through production deployment, monitoring, and iterative improvement. - Own and evolve the team's production and experimentation infrastructure, including deployment pipelines, observability and tracing, and evaluation harnesses. - Drive applied research by identifying and pursuing emerging techniques, and disseminate findings through internal and external publications, talks, and journal clubs. - Raise the technical bar across the team, including mentor junior scientists and engineers, review designs and code, and help shape the product and technical roadmap. A day in the life As an Applied Scientist, you will own ambiguous, high-impact problems and help shape the technical roadmap that connects risk to Amazon. You will drive AI products that make audit work more effective and efficient, increasingly centered on LLM and agentic systems. You set technical direction across the full arc of applied science. That means framing problems, making architecture decisions, defining how the team evaluates quality, and delivering solutions in production. The ideal candidate pairs deep machine learning expertise with a builder's instinct for production architecture. They thrive on ambiguity, mentor others, and follow a fast-moving research frontier. About the team Internal Audit’s mission is to help our businesses improve controllership, operational efficiency, and customer experience.

Amazon’s Customer Behavior Analytics org is looking for an Applied Scientist to spearhead the rapid growth of our Marketing Measurement solutions. The team focuses on building scalable ML and causal inference solutions to estimate the effectiveness of Amazon marketing efforts and provide actionable insights to the various marketing teams within Amazon. This is a high-impact role with opportunities to develop systems that affect investments to the size of billions of dollars. We work closely with business stakeholders and strive to continuously produce tangible impact on the company’s strategic and tactical planning and operations. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to detail. You should be able to translate how data represents the customer journey, be comfortable dealing with large and complex data sets, and have experience using machine/deep learning at scale to solve business problems. You should have strong analytical and communication skills, be able to work with product managers and software teams to define key business questions and work with the analytics team to solve them. You will apply your expertise in ML/DL, statistics, and data-wrangling to identify opportunities for further research and to provide insights that drive larger initiatives. You will join a highly collaborative and diverse working environment that will empower you to shape the future of Amazon marketing, as well allow you to be part of the large science community within the Customer Behavior Analytics (CBA) organization. Key job responsibilities The main responsibilities for this position include: - Apply your expertise in Transformer models, LLMs, ML/DL and statistical modeling to develop solutions and systems that describe how Amazon’s marketing campaigns impact customers’ actions - Own the end-to-end development of novel causal inference models that address the most pressing needs of our business stakeholders and help guide their future actions - Improve upon and simplify our existing solutions and frameworks - Review and audit modeling processes and results from other scientists, both junior and senior - Work with marketing leadership to align our measurement plan with business strategy - Formalize assumptions about how our models are expected to behave and explain why they are reasonable - Identify new opportunities that are suggested by the data insights - Bring a department-wide perspective into decision making - Develop and document scientific research to be shared with the greater science community at Amazon About the team The Customer Behavior Analytics (CBA) organization owns Amazon’s insights pipeline, from data collection to deep analytics. We aspire to be the place where Amazon teams come for answers, a trusted source for data and insights that empower our systems and business leaders to make better decisions. Our outputs shape Amazon product and marketing teams’ decisions and thus how Amazon customers see, use, and value their experience.

Worldwide Global Selling has been helping individuals and businesses increase sales and reach new customers around the globe. Today, more than 50% of Amazon's total unit sales come from third-party selection. The Global Selling team in China is responsible for recruiting local businesses to sell on Amazon's 19+ overseas marketplaces and supporting local Sellers' success and growth on Amazon. Our vision is to be the first choice for all types of Chinese business to go globally. The Worldwide Global Selling Analytics, Intelligence, and Technology (WWGS-AIT) team serves as the research, automation, and insight arm of the International Seller Service data hub, enabling rapid delivery of growth insights through strategic investments in regional data foundations, self-service business intelligence solutions, and artificial intelligence tools. The WWGS-AIT team is positioned to establish AI-ready foundational capabilities across the WWGS organization while maintaining excellence in business insight generation, and self-service BI/AI application development. WWGS-AIT is looking for a Data Scientist to build reusable science capabilities that support seller growth, operational decision-making, and cross-domain innovation across Worldwide Global Selling. You will lead high-impact modeling initiatives at the intersection of graph science, machine learning, simulation, and optimization. Your initial focus will include building identity-resolution and entity-linkage capabilities that create a trusted One ID view across fragmented seller and business data; developing simulation and decision models for logistics and inventory options; and establishing reusable modeling foundations for seller lifecycle and other cross-domain use cases. You will also partner with business, product, engineering, and analytics teams to evaluate and deliver prioritized science opportunities through a common intake process. This role is ideal for a hands-on scientist who can move from ambiguous business problems to robust, production-ready models and decision systems. Key job responsibilities Lead the design, development, and productionization of graph-based identity-resolution and entity-linkage models that connect seller, account, business, logistics, and other relevant entities into a trusted One ID foundation. Develop simulation, optimization, forecasting, and decision-support models for logistics, inventory, and related operational choices; quantify trade-offs, uncertainty, and expected business impact. Establish scalable model-development practices, including feature engineering, experiment design, model validation, monitoring, reproducibility, documentation, and responsible-use controls. Translate ambiguous business questions into clear science problems, measurable hypotheses, model requirements, and decision frameworks. Partner with Data Engineering, BIE, Product, and domain teams to build reliable data pipelines, model features, evaluation datasets, and production model interfaces. Support prioritized science needs from Supply Chain, Seller Success, and other teams through the WWGS-AIT operating-planning intake and prioritization process. Define model performance, business-impact, and operational-success metrics; use offline evaluation, back-testing, simulation, and controlled experiments to continuously improve solutions. Contribute applied AI and GenAI expertise where it improves science-enabled products—for example, model evaluation, retrieval/ranking, intelligent decision support, or AI-agent capabilities grounded in trusted data and models. Influence the WWGS science roadmap by identifying opportunities to convert repeated business problems into durable, reusable data and modeling capabilities.