Careers

Amazon has the world’s most complex supply chain: we fulfill global demand for hundreds of millions of products at lightning fast delivery speeds. A core part of the operations is forecasting: We forecast the demand of hundreds of millions of products up to a year into the future. The forecasts are used to automatically order hundreds of millions worth of inventory weekly, decide where to place that inventory, and to establish labor plans for hundreds of warehouses. The Applied Scientist II will work with the Supply Chain Optimization Technologies (SCOT) Forecasting team and both business and engineering stakeholders worldwide to develop state of art machine learning models to forecast inbound shipment. The scientist develops novel algorithmic architectures, toward the ultimate goal of accurately predicting when and how Amazon receives shipments of millions of products world-wide. This drives down costs and enables the offer of lower prices and better in-stock selection for our customers. Working collaboratively, you will develop solutions to complex problems, such as designing the next generation of algorithms. As an Applied Scientist, you will continue to contribute to the research community, by working with other scientists across Amazon, as well as collaborating with academic researchers and publishing papers. Within SCOT Forecasting, our Science community values teamwork and recognizes the need to take chances and try new ideas that may fail. Furthermore, our builder culture means that Scientists and Software Development Engineers work closely together to invent and construct at a massive scale. Your work can be part of Amazon production system and result in concrete business impact. Key job responsibilities - Design, implement, and evaluate innovative models, agents, and software prototypes. - Collaborate with a team of experienced scientists to drive technological advancements. - Develop novel solutions to complex business problems in collaboration with partner teams. - Constructively critique peer research and mentor junior scientists and engineers - Contribute to Amazon's global science community through collaboration and publication of groundbreaking research. About the team Supply Chain Optimization Technologies (SCOT) owns Amazon’s global inventory planning systems. We decide what, when, where, and how much we should buy to meet Amazon’s business goals and to make our customers happy. We decide how to place and move inventory within Amazon’s fulfillment network. We do this for hundreds of millions of items and hundreds of product lines worth billions of dollars of world-wide. Venturing beyond traditional operations research methods for sequential decision-making in inventory planning. The team combines empirical research and real world testing, backed by a robust theoretical foundation.

Are you passionate about AI for recommendation systems? Do you want to influence the content that customers see at Amazon.com? Our recommendation services team designs and implements scalable machine learning solutions to personalize and optimize customer experience across Amazon retail pages. We are currently expanding in New York, and are looking for an applied scientist to join us in this exciting journey. As an Applied Scientist, you will: - Push the boundaries of real-world ranking, recommendation, and optimization systems - Support science, engineering and product development on a scale only seen at Amazon. - Champion and define best practices to maximize learnings while mentoring more junior scientists and engineers. - Obsess over customer needs and satisfaction. - Create intellectual property, influence others while demonstrating significant creativity and being vocally self-critical. - Shape product definitions and objective and surface signals on how these objectives meet long term customer needs. - Translate metrics & signals into actionable plans to calibrate individual components. - Operate hands-on and as an implementor of algorithms and models delivered to production systems. - Help define customer focused research initiatives. Please visit https://www.amazon.science for more information.

Device Economics is looking for an economist experienced in causal inference, empirical industrial organization, forecasting, and scaled systems to work on business problems to advance critical resource allocation and pricing decisions in the Amazon Devices org. Output will be included in scaled systems to automate existing processes and to maximize business and customer objectives. Amazon Devices designs and builds Amazon first-party consumer electronics products to delight and engage customers. Amazon Devices represents a highly complex space with 100+ products across several product categories (e-readers [Kindle], tablets [Fire Tablets], smart speakers and audio assistants [Echo], wifi routers [eero], and video doorbells and cameras [Ring and Blink]), for sale both online and in offline retailers in several regions. The space becomes more complex with dynamic product offering with new product launches and new marketplace launches. The Device Economics team leads in analyzing these complex marketplace dynamics to enable science-driven decision making in the Devices org. Device Economics achieves this by combining economic expertise with macroeconomic trends, and including both in scientific applications for use by internal analysts, to provide deep understanding of customer preferences. Our team’s outputs inform product development decisions, investments in future product categories, product pricing and promotion, and bundling across complementary product lines. We have achieved substantial impact on the Devices business, and will achieve more. Device Economics seeks an economist adept in measuring customer preferences and behaviors with proven capacity to innovate, scale measurement, and drive rigor. The candidate must be passionate about advancing science for business and customer impact.

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches

AWS Support is looking for a high caliber Applied Scientist to build AI/GenAI experiences and foundations for Kumo Intelligent Tooling — the organization that owns products and services used by 10,000+ AWS Support staff to help 40,000+ unique monthly AWS customers, and is responsible for AWS Support’s strategic initiative to transform its business and delivery model through GenAI. We are looking for a Principal Scientist to join us and spearhead the AI revolution through intelligent solutions that assist customers and Support staff to troubleshoot and resolve technical issues. You are a hands-on contributor and will apply your knowledge to propose solutions, create software prototypes, and productize prototypes into production systems using modern software development tools and methodologies. In addition, you will support and scale your solutions to meet ever-growing customer needs use cases. You have strong verbal and written communication skills, are self-driven and deliver high quality results in a fast-paced environment. You will play a pivotal role in shaping the definition, vision, design, roadmap and development of solutions from beginning to end for hard, previously unsolved problems. Key job responsibilities - Lead and conduct advanced research in Large Language Models (LLMs), GenAI, and Deep Learning, with a focus on developing novel algorithms, architectures, and methodologies for technical support. - Stay up-to-date with the latest advancements in AI, LLMs, and GenAI, and identify opportunities to leverage cutting-edge technologies to deliver new Support offerings and capabilities to AWS customers. - Lead by example, demonstrating technical excellence that other scientists aspire to follow, fostering a culture of innovation and knowledge sharing, and taking time to mentor and develop team members - Collaborate with cross-functional teams, including scientists, engineers, and product managers, to translate research findings into practical applications. - Partner across AWS AI/GenAI service teams to influence and drive investment prioritization and product direction. - Evangelize our AI/GenAI innovations, results, and impact to customers, partners, and AWS senior leaders. About the team Kumo is the global product and engineering organization for AWS Support, a multi-billion $ business. Our mission is to empower innovators to get the most out of cloud services. We build technology that reimagines how people and automation combine to solve problems, remove risks, build with excellence, and drive business impact. We own critical cloud services used by all AWS customers to build, optimize, and operate at scale, including AWS Health, Trusted Advisor, Well-Architected, re:Post, Support Center, and AWS Managed Services. We also own services that enable AWS support teams to provide mission-critical, customer-obsessed support to our customers, including Command Center (the console platform for 14,000 frontline staff and technical account managers), Kumo Case Management (the contact center platform for technical support), Tool Contribution (the platform for support staff to build and reuse troubleshooting tools), and Business Case Authorization (the service for controlling access to customer metadata based on business justifications). AWS Kumo is a dynamic, agile, and collaborative team of individuals with diverse backgrounds, located around the globe with larger teams in the U.S., Canada, and South Africa.

The Amazon Search team creates powerful, customer-focused search and advertising solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide. Amazon has grown rapidly and will continue to do so in foreseeable future. Providing a high quality search experience is a unique challenge as Amazon expands to new customers, countries, categories, and product lines. We are seeking a strong applied scientists to join the newly formed Relevance India team. This team’s charter is to increase the pace at which Amazon expands and improve the search experience at launch. In practice, we aim to invent universally applicable signals and algorithms for training machine-learned ranking models and improve the machine-learning framework for training and offline evaluation that is used for all new relevance models. Key job responsibilities * Build machine learning models for Product Search. * Develop new ranking features and techniques building upon the latest results from the academic research community. * Propose and validate hypothesis to direct our business and product road map. Work with engineers to make low latency model predictions and scale the throughput of the system. * Focus on identifying and solving customer problems with simple and elegant solutions. * Design, develop, and implement production level code that serves billions of search requests. Own the full development cycle: design, development, impact assessment, A/B testing (including interpretation of results) and production deployment. * Collaborate with other engineers and related teams within A9.com and Amazon.com to find technical solutions to complex design problems. * Take ownership. Understand the needs of various search teams, distill those into coherent projects, and implement them with an eye on long-term impact. * Be a leader. Use your expertise to set a high bar for the team, mentor team members, set the tone for how to take on and deliver on large impossible-sounding projects. * Be ambitious. Find and eagerly tackle hard problems. * Be curious. You will work alongside systems engineers, machine learning scientists, and data analysts. Your effectiveness and impact will depend on discussing problems with and learning from them. You will have access to the cutting-edge technologies and vast technical tools and resources of Amazon and will need to learn how to use them effectively. * Be customer focused. Work backwards from customer problems, figure out elegant solutions, and implement them for speed and scalability.