Careers

Economists in Delivery Experience (DEX) solve key business problems in operations and help drive innovation in Amazon's retail supply chain. You'll apply frontier economic methods to product design, pricing, personalization, program evaluation, and more. You'll build econometric models using our world-class data systems and apply economic theory to solve business problems in a fast-moving environment. Economists at Amazon develop novel approaches to solve quantitative problems and contribute to the design of automated systems across the company. We're looking for a Senior Economist with a background in causal analysis to lead studies related to innovative delivery programs. Potential projects include understanding how customers value programs that enable coordinated package delivery, scheduled delivery, and sustainability in delivery services. To be successful in this role, you'll need to communicate effectively with product and tech teams, work closely with partners across DEX and Amazon, and translate data-driven findings into actionable insights. You'll thrive if you enjoy tackling ambiguous challenges using the economics toolkit and identifying and solving problems at scale. We have a supportive, fast-paced team culture, and we prioritize learning, growth, and helping each other continuously raise the bar. Key job responsibilities Lead data-driven econometric studies to create future business opportunities Consult with stakeholders in Delivery Experience and other teams to help solve existing business challenges Independently identify and pursue new opportunities to leverage economic insights Advise senior leaders and collaborate with other scientists to drive innovation Support innovative delivery program growth worldwide Write business and technical documents communicating business context, methods, and results to business leadership and other scientists Serve as a technical lead and mentor for junior scientists, ensuring a high science bar Serve as a technical reviewer for our team and related teams, including document and code reviews

Are you excited to work on cutting edge product-driven generative AI solutions to help customers discover the hottest and best reviewed products? The Discovery Tech team helps customers discover and engage with new customer experience with popular and relevant products across Amazon worldwide. We do this by combining technology, science, and innovation to build new customer-facing features and experiences alongside cutting edge tools for marketers. You will be responsible for creating and building critical services that automatically generate, target, and optimize Amazon’s cross-category marketing and merchandising. Through the enablement of intelligent marketing campaigns that leverage machine-learning models, you will help to deliver the best possible shopping experience for Amazon’s customers all over the globe. We are looking for analytical problem solvers who enjoy diving into data, excited about data science and statistics, can multi-task, and can credibly interface between engineering teams and business stakeholders. Your analytical abilities, business understanding, and technical savvy will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your domain spans the design, development, testing, and deployment of data-driven and highly scalable machine learning solutions in product recommendation. As an Applied Scientist III, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. To know more about Amazon science, please visit https://www.amazon.science

As a Principal Scientist in Ring and Blink, you will have deep subject matter expertise in the areas of Artificial Intelligence. You will work with multiple teams of scientists and engineers to translate business and functional requirements into concrete deliverables. More specifically, you will provide thought leadership on and lead strategic efforts in Computer Vision (CV) and Machine Learning (ML) areas for Ring and Blink, covering themes including but not limited to Classic Deep Learning, Large Language Model, AI Safety, and Bias/Fairness, and work with Product, Science, and Engineering teams to deliver short- and long-term CV/ML solutions in production for optimized edge-based solutions to large-scale models. Finally, you will work with academic partners to support our in-house talent with direct access to cutting edge research and mentoring.

Do you enjoy solving challenging problems and driving innovations in research? Are you seeking for an environment with a group of motivated and talented scientists like yourself? Do you want to create scalable optimization models and apply machine learning techniques to guide real-world decisions? Do you want to play a key role in the future of Amazon transportation and operations? Come and join us at Amazon's Modeling and Optimization team (MOP). Key job responsibilities An Applied Scientist in the Modeling and Optimization (MOP) team - provides analytical decision support to Amazon planning teams via applying advanced mathematical and statistical techniques. - collaborates effectively with Amazon internal business customers, and is their trusted partner - is proactive and autonomous in discovering and resolving business pain-points within a given scope - is able to identify a suitable level of sophistication in resolving the different business needs - is confident in leveraging existing solutions to new problems where appropriate and is independent in designing and implementing new solutions where needed - is aware of the limitations of their proposed solutions and is proactive in communicating them to the business, and advances the application of sciences towards Amazon business problems by bringing new methods, ideas, and practices to the team and scientific community. A day in the life - Your will be developing model-based optimization, simulation, and/or predictive tools to identify and evaluate opportunities to improve customer experience, network speed, cost, and efficiency of capital investment. - You will quantify the improvements resulting from the application of these tools and you will evaluate the trade-offs between potentially competing objectives. - You will develop good communication skills and ability to speak at a level appropriate for the audience, will collaborate effectively with fellow scientists, software development engineers, and product managers, and will deliver business value in a close partnership with many stakeholders from operations, finance, IT, and business leadership. About the team - At the Modeling and Optimization (MOP) team, we use mathematical optimization, algorithm design, statistics, and machine learning to improve decision-making capabilities across WW Operations and Amazon Logistics. - We focus on transportation topology, labor and resource planning for fulfillment facilities, routing science, visualization research, data science and development, and process optimization. - We create models to simulate, optimize, and control the fulfillment network with the objective of reducing cost while improving speed and reliability. - We support multiple business lanes, therefore maintain a comprehensive and objective view, coordinating solutions across organizational lines where possible.

The North America Capacity Planning team in Amazon Stores' Supply Chain organization is charged with planning, managing, and optimizing the use of inbound, storage, and fulfillment capacity in North America. Optimal planning and usage of this capacity is critical in enabling us to deliver several billion items annually at continually lower costs and higher speeds. The decision space extends from tactical short-term actions (what capacity constraints do we face next week and how do we alleviate them?) to large-scale long-term strategy (what kind of fulfillment capacity should we invest in, where, over the next three years?). The team works in close partnership with several other business and tech teams to develop capacity plans, and ensure the delivery and optimal use of fulfillment capacity. As a Principal Research Scientist in this team, you will interact with subject matter experts within and outside the team, as well as scientists in a range of partner organizations, to develop scientific models for capacity planning and optimization. You will leverage these models to influence senior leadership to make high-impact decisions that are critical to building Amazon's future supply chain. You will also be an active participant in the overall Amazon scientific community, generating intellectual capital, participating in reviews and conferences, publishing in internal and external venues, and incorporating scientific rigor in the team's practices. Key job responsibilities - Develop scientific models for large-scale fulfillment capacity planning under uncertainty - Partner with stakeholder science teams for development and use of their scientific models for various aspects of capacity planning such as demand estimation, inventory flows, and multi-echelon supply chain management - Develop strategic plans for long-term capacity investments, in close collaboration with subject matter experts within and outside the team - Influence senior leadership on large-scale one-way capital and technological investments for designing our future fulfillment network - Partner with scientists across Amazon in developing scientific models that jointly optimize the Amazon supply chain

The Amazon Private Brands’ Discovery team develops innovative machine learning solutions to enhance customer awareness of Amazon's private brands and help customers discover products they love. This interdisciplinary team of scientists and engineers incubates and builds disruptive technologies, tackling some of the most complex scientific challenges at Amazon. To achieve this, we leverage a wide range of advanced techniques, including Natural Language Processing (NLP), deep learning, large language models (LLMs), multi-armed bandits, reinforcement learning, Bayesian optimization, causal and statistical inference, and econometrics, to drive discovery throughout the customer journey. Our solutions are not only critical to the success of Amazon's private brands but also serve as a leading example of discovery technologies across Amazon. We are seeking a Principal Scientist to join our team and lead the development of cutting-edge machine learning models and algorithms that will drive innovative discovery solutions for Amazon Private Brands. In this hands-on role, you will apply your expertise to propose solutions, create software prototypes, and successfully transition these prototypes into production systems using modern software development tools and methodologies. You will also be responsible for scaling these solutions to meet the growing and evolving needs of our customers. As a Principal Scientist, you will play a pivotal role in shaping the vision, design, and roadmap for complex, previously unsolved problems, guiding solutions from inception to implementation. Strong verbal and written communication skills, a self-driven mindset, and the ability to deliver high-quality results in a fast-paced environment are essential for success in this role. Key job responsibilities You will be responsible for defining key research directions, adopting or inventing new machine learning techniques, conducting rigorous experiments, publishing results, and ensuring that research is translated into practice. You will develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them. You will also participate in organizational planning, hiring, mentorship and leadership development. You have a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance).

Our team's mission is to improve Shopping experience for customers interacting with Amazon devices via voice. We work with Alexa and multiple other teams to research and develop advanced state-of-the-art speech technologies. Do you want to be part of the team developing the latest technology that impacts the customer experience of ground-breaking products? Then come join us and make history. Key job responsibilities We are looking for a passionate, talented, and inventive Research Scientist with a background in Machine Learning to help build industry-leading Speech and Language technology. As a Research Scientist at Amazon you will work with talented peers to develop novel algorithms and modelling techniques to drive the state of the art in speech synthesis. Position Responsibilities: * Participate in the design, development, evaluation, deployment and updating of data-driven models for Speech and Language applications. * Participate in research activities including the application and evaluation of Speech and Language techniques for novel applications. * Research and implement novel ML and statistical approaches to add value to the business.

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! SB ResponseModel team's role is to support the prediction of Click Through Rates, View Rates, and Conversion Rates for Sponsored Brands. Our goal is to enhance advertising efficiency, improve the shopper experience, and promote SB product recognition. We collaborate with different teams across the Amazon Ads to build scalable online and offline ML infrastructure systems to accelerate science innovations, facilitate business growth and promote technology innovation. As an Applied Scientist on this team, you will: - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Research new and innovative machine learning approaches. - Recruit Applied Scientists to the team and provide mentorship. Impact and Career Growth You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven fundamentally from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.

The Private Brands Discovery team designs innovative machine learning solutions to drive customer awareness for Amazon’s own brands and help customers discover products they love. Private Brands Discovery is an interdisciplinary team of Scientists and Engineers, who incubate and build disruptive solutions using cutting-edge technology to solve some of the toughest science problems at Amazon. To this end, the team employs methods from Natural Language Processing, Deep learning, multi-armed bandits and reinforcement learning, Bayesian Optimization, causal and statistical inference, and econometrics to drive discovery across the customer journey. Our solutions are crucial for the success of Amazon’s own brands and serve as a beacon for discovery solutions across Amazon. This is a high visibility opportunity for someone who wants to have business impact, dive deep into large-scale problems, enable measurable actions on the consumer economy, and work closely with scientists and engineers. As a scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.. With a focus on bias for action, this individual will be able to work equally well with Science, Engineering, Economics and business teams. Key job responsibilities - Drive applied science projects in machine learning end-to-end: from ideation over prototyping to launch. For example, starting from deep scientific thinking about new ways to support customers’ journeys through discovery, you analyze how customers discover, review and purchase Private Brands to innovate marketing and merchandising strategies. - Propose viable ideas to advance models and algorithms, with supporting argument, experiment, and eventually preliminary results. - Invent ways to overcome technical limitations and enable new forms of analyses to drive key technical and business decisions. - Present results, reports, and data insights to both technical and business leadership. - Constructively critique peer research and mentor junior scientists and engineers. - Innovate and contribute to Amazon’s science community and external research communities.

The Private Brands Discovery team designs innovative machine learning solutions to enhance customer awareness of Amazon’s own brands and help customers find products they love. This interdisciplinary team of scientists and engineers incubates and develops disruptive solutions using cutting-edge technology to tackle some of the most challenging scientific problems at Amazon. To achieve this, the team utilizes methods from Natural Language Processing, deep learning, large language models (LLMs), multi-armed bandits, reinforcement learning, Bayesian optimization, causal and statistical inference, and econometrics to drive discovery throughout the customer journey. Our solutions are crucial to the success of Amazon’s private brands and serve as a model for discovery solutions across the company. This role presents a high-visibility opportunity for someone eager to make a business impact, delve into large-scale problems, drive measurable actions, and collaborate closely with scientists and engineers. As a team lead, you will be responsible for developing and coaching talent, guiding the team in designing and developing cutting-edge models, and working with business, marketing, and software teams to address key challenges. These challenges include building and improving models for sourcing, relevance, and CTR/CVR estimation, deploying reinforcement learning methods in production etc. In this role, you will be a technical leader in applied science research with substantial scope, impact, and visibility. A successful team lead will be an analytical problem solver who enjoys exploring data, leading problem-solving efforts, guiding the development of new frameworks, and engaging in investigations and algorithm development. You should be capable of effectively interfacing between technical teams and business stakeholders, pushing the boundaries of what is scientifically possible, and maintaining a sharp focus on measurable customer and business impact. Additionally, you will mentor and guide scientists to enhance the team's talent and expand the impact of your work.