Careers

We are seeking a seasoned executive leader to join our Network Solutions team within Amazon Customer Service, as Director, Data Science. In this role you will build and lead a data, analytics, and measurement science function that orchestrates Amazon's Customer Service full network – across our human-assisted and customer-facing automation AI-enabled channels – as a single intelligent system. Network Solutions owns demand forecasting, network planning, routing, real-time observability, and workforce strategy, and is building the next generation of AI-enabled planning systems with intelligent and adaptive capabilities that consider humans and AI-agents, simultaneously. You will lead a multi-disciplinary team – scientists, data engineers, business intelligence engineers, and analysts – who will build the data strategy these new systems require and the measurement science that reshapes how every network decision is made. At the core is a unified measurement framework that measures the value of experiences across customers, associates, and our business, drawing on causal inference, economics, operations, and behavioral science. You will create the underlying logic for how we plan capacity, route customers, develop associates, and allocate resources – and serve as an interface and work closely with partner teams within Customer Service, including the CS-wide Data Intelligence organization. The ideal candidate brings deep cross-disciplinary experience in data science, measurement, and operations – a track record of building and leading data and insights organizations from 0-to-1, the scientific depth to drive novel measurement frameworks, and the rare ability to blend quantitative rigor with an understanding of human systems – developing the right logic to drive decision-making at scale. Key job responsibilities - Build and lead the Network Solutions data and analytics organization: provide unified leadership across business intelligence engineering, data engineering, analytics, and reporting; recruit, grow, and retain a team spanning measurement scientists, data engineers, and analysts, building the function from 0-to-1 - Drive the measurement science and decisioning core: develop the causal inference-based framework that quantifies value creation across multiple dimensions; establish methodology standards, signal definitions, and utility function design that give Network Solutions a unified view of performance - Own the Network Solutions data foundation: own critical signal pipelines, govern data quality, and build a composable data architecture that all Network Solutions product and science teams build on; ensure measurements are interoperable across planning, routing, incident management, and workforce systems - Integrate measurement science into production systems: demonstrate the shift to value-informed decision-making; partner with product and engineering teams to embed measurement frameworks into planning, routing, and resource allocation - Interface with Amazon CS and company-wide data, analytics, and science organizations: represent Network solutions as a peer partner to central data infrastructure, customer experience measurement, and applied science functions; consolidate Network Solutions data demand into a clear, prioritized voice - Drive adoption of data standards and measurement frameworks: translate complex models and multi-dimensional utility functions into intuitive, actionable insights for non-technical stakeholders; establish shared measurement standards that teams across the organization can build on - Develop the long-term data and science roadmap: anticipate future needs as the organization scales; identify opportunities to extend measurement capabilities beyond Network Solutions About the team Network Solutions sits at the intersection of customer experience and associate experience within Amazon Customer Service, owning the logic and infrastructure that determines how those two sides are balanced, served, and optimized together. On one side is the customer: their experience, their journey, and the AI automation that increasingly shapes both. On the other side is the associate: their work, their well-being, and the conditions that make them effective. Network Solutions sits at the center, owning demand forecasting, network planning, routing and matching, real-time observability, and workforce strategy – including the long-term design of the network itself as the balance between AI and human engagement continues to evolve. We are building the next generation of systems that will transform how Customer Service operates using intelligent, adaptive capabilities that learn and improve continuously.

Are you interested in big data, Machine Learning, and building recommendation services using Generative AI? If so, Amazon's Personalization team might be the right place for you. Key job responsibilities - Use AI and machine learning to create scalable solutions for business problems. - Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes. - Design, develop and evaluate highly scalable models for predictive learning. - Work closely with software engineering teams to drive model implementations and new feature creations. - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Research and implement novel machine learning and statistical approaches - Review peer work and provide feedback A day in the life You are an Applied Scientist who loves big data and passionate about improving customer shopping experience by inventing and applying state-of-art technologies (e.g., LLMs/SLMs, Machine Learning, Natural Language Processing, and Computer Vision) to build the next-generation product recommendation engine for Amazon. You have an entrepreneurial spirit, know how to deliver, are deeply technical and highly innovative. You work closely with software engineers to put algorithms into production. You also work in partnership with teams across Amazon to create enormous benefits for our customers. About the team We are part of Amazon’s Personalization organization, a high-performing group with a huge impact on hundreds of millions of customers, innovating at the intersection of customer experience, machine learning, and large-scale distributed systems. We run global experiments and our work has revolutionized e-commerce with features such as "Compare with similar items", "Keep Shopping For", “Customers who bought this item also bought”, and, “Frequently bought together” among others.

We are seeking a Data Scientist to join our analytics team. This person will own the design and implementation of scalable and reliable approaches to support or automate decision making throughout the business. You will do this by analyzing data with a variety of statistical techniques and then building, validating, and implementing models based your analysis. You will not be able to do this alone but by building partnerships across data, engineering, and business teams. Key job responsibilities Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult customer or business problems and cases in which the solution approach is unclear. Proactively seek to identify business opportunities and insights and provide solutions to automate and optimize key internal and external products based on a broad and deep knowledge of Amazon data, industry best-practices, and work done by other teams. Dive deep into the data and other models across the business to identify defects or inefficiencies which materially impact the customer or business, but can be mitigated through corrective actions for the AB Ops use case Acquire this data by accessing data sources and building the necessary SQL/ETL queries or scripts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models and automated tools using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate these models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement these models in a manner which complies with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production. Enable product engineering teams to consume your models through services which can directly power customer-facing experiences. Inspect the key business metrics/KPIs (even if you did not create them) when your analytics work points to potential gaps or opportunities; providing clear, compelling analyses by leveraging your knowledge across the AWS suite of products to support the broader business. About the team Amazon Business (AB) launched in the United States in April 2015 with the vision of delivering a comprehensive procurement solution for business customers of all sizes. In the US, AB now serves Fortune 500 companies, hospital systems, local governments, and education organizations. To support AB's mission, the AB Operations team owns end to end customer experience for AB customers right from checkout to final mile delivery and sits under the broader Global Transportation Services (GTS). We are the Single Threaded WW Ops team driving B2B operations, developing products and programs unique to business customers, and enhancing the core Amazon delivery capabilities for commercial addresses. While Amazon has built its core operations over the last two decades mostly catered towards the end-consumer, we recognize that the business customers have unique needs and require a different set of features and services from their e-Commerce provider.

Do you thrive in generating actionable insights at scale from complex datasets? Can you use data to look around the corners of a business and guide leaders on impactful business decisions? The Shop and Keep Data and Science team builds internet-scale shopping intelligence that helps Amazon customers find what they want with ease and give them the right information to make confident and lasting purchases. Our work powers customer experiences across Amazon.com surfaces, including Search, Product Detail Pages, Recommendations and Ads. We are looking for an experienced Senior Data Scientist to lead high visibility science initiatives that guide the business and ensure quality of our shopping intelligence signals. This role will focus on Data Science and Analytics related to signal quality evaluation, impact measurement, and delivering business insights. The role requires deep technical skills, strong business acumen and a deep analytical background to provide actionable data-driven insights and decision support. As a member of this team, the role will dive deep into our data and leverage their strong skills in measurement science, LLMs, predictive and prescriptive models, data ETLs, dashboards and visualization to support business strategy and deliver goals with executive visibility. You must have the ability to communicate effectively across multiple technical and non-technical business units, as well as across other geographies. Successful members of this team collaborate effectively to solve data problems, are highly organized and detail-oriented, implement new solutions for complex problems, and deliver successfully against highest standards. The ideal candidate will be a highly motivated individual that seeks to “tell the story” of the data with little direction or supervision. Key job responsibilities • Leverage state-of-the-art measurement science approaches, including LLMs, to develop evaluation frameworks for a variety of signals and customer experiences. • Support the development of continuously-evolving business analytics and data models, own the quantitative analysis of the performance of our features. • Continually develop new ways of using data to look around the corners and guide the development of shopping intelligence signals. • Use LLMs, machine learning, data mining, and statistical techniques to design/run experiments that solve complex business problems. • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. • Develop a deep understanding of e-commerce metrics, reporting tools, and data structures in order to identify and drive resolution of issues, provide actionable intelligence with existing metrics or identify, develop, and propose new metrics, dashboards, scorecards or new tools. • Develop relationships and processes with partner teams, PMs/TPMs, engineers, and other functional teams to identify and address reporting issues. • Manage and develop advanced analytical tools that align, and simplify, monthly business reviews, annual planning, operations and forecasting processes, based on the needs of the business and stakeholders. About the team The Shop and Keep Data and Science team's mission is to build shopping intelligence that helps Amazon customers find what they want with ease and give them the right information to make confident and lasting purchases. Through our personalized discovery and product evaluation signals, we assist customers in their shopping missions as they navigate Amazon.com's surfaces. We also share a selection of signals with partner teams and sellers to establish closed loop mechanisms and drive improvements in our signals, customer outcomes and Amazon’s product selection.

Are you passionate about solving unique customer-facing problems in the Amazon scale? Are you excited about utilizing statistical analysis, machine learning, data mining and leverage tons of Amazon data to learn and infer customer shopping patterns? Do you enjoy working with a diversity of engineers, machine learning scientists, product managers and user-experience designers? If so, you have found the right match! Fashion is extremely fast-moving, visual, subjective, and it presents numerous unique problem domains such as product recommendations, product discovery and evaluation. The vision for Amazon Fashion is to make Amazon the number one online shopping destination for Fashion customers by providing large selections, inspiring and accurate recommendations and customer experience. The NAS Shop and Keep (SHAKE) organization empowers customers to make confident, lasting purchase decisions by minimizing purchase anxiety and reducing returns. The NAS SHAKE science team advances this mission by innovating and developing scalable ML solutions tailored to these unique challenges. The team is hiring a Data Scientist who has a solid background in Statistical Analysis, Machine Learning, GenAI applications and Data Mining and a proven record of effectively analyzing large complex heterogeneous datasets, and is motivated to grow professionally as a Data Scientist. Key job responsibilities - You will work on our Science team and partner closely with applied scientists, data engineers as well as product managers, UX designers, and business partners to answer complex problems via data analysis. Outputs from your analysis will directly help improve the performance of the ML based recommendation systems thereby enhancing the customer experience as well as inform the roadmap for science and the product. - You can effectively analyze complex and disparate datasets collected from diverse sources to derive key insights, build segmentation models and improve customer experience by reducing retail returns. - You have excellent communication skills to be able to work with cross-functional team members to understand key questions and earn the trust of senior leaders. - You are able to multi-task between different tasks such as gap analysis of algorithm results, integrating multiple disparate datasets, doing business intelligence, analyzing engagement metrics or presenting to stakeholders. - You thrive in an agile and fast-paced environment on highly visible projects and initiatives.

Are you passionate about solving unique customer-facing problems in the Amazon scale? Are you excited about utilizing statistical analysis, machine learning, data mining and leverage tons of Amazon data to learn and infer customer shopping patterns? Do you enjoy working with a diversity of engineers, machine learning scientists, product managers and user-experience designers? If so, you have found the right match! Fashion is extremely fast-moving, visual, subjective, and it presents numerous unique problem domains such as product recommendations, product discovery and evaluation. The vision for Amazon Fashion is to make Amazon the number one online shopping destination for Fashion customers by providing large selections, inspiring and accurate recommendations and customer experience. The NAS Shop and Keep (SHAKE) organization empowers customers to make confident, lasting purchase decisions by minimizing purchase anxiety and reducing returns. The NAS SHAKE science team advances this mission by innovating and developing scalable ML solutions tailored to these unique challenges. The team is hiring a Data Scientist who has a solid background in Statistical Analysis, Machine Learning, GenAI applications and Data Mining and a proven record of effectively analyzing large complex heterogeneous datasets, and is motivated to grow professionally as a Data Scientist. Key job responsibilities - You will work on our Science team and partner closely with applied scientists, data engineers as well as product managers, UX designers, and business partners to answer complex problems via data analysis. Outputs from your analysis will directly help improve the performance of the ML based recommendation systems thereby enhancing the customer experience as well as inform the roadmap for science and the product. - You can effectively analyze complex and disparate datasets collected from diverse sources to derive key insights, build segmentation models and improve customer experience by reducing retail returns. - You have excellent communication skills to be able to work with cross-functional team members to understand key questions and earn the trust of senior leaders. - You are able to multi-task between different tasks such as gap analysis of algorithm results, integrating multiple disparate datasets, doing business intelligence, analyzing engagement metrics or presenting to stakeholders. - You thrive in an agile and fast-paced environment on highly visible projects and initiatives.

Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making. Key job responsibilities * Define and implement new automated reasoning features that employ scalable and efficient approaches to solve complex problems using neural learning and symbolic/formal reasoning * Apply software engineering best practices to ensure a high standard of quality for all team deliverables * Work in an agile, startup-like development environment * Deliver high-quality scientific artifacts * Work with the team to help drive business decisions About the team About the team Why AWS? 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 Here at AWS, 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. 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.

Amazon is seeking a motivated candidate to provide insights into opportunities for operational improvement through analytics. Role will require analysis, understanding of the systems workflow, cross-functional communication and issues management. The successful candidate is passionate about optimizing business processes and performance metrics, and takes a driven approach to identifying and prioritizing the most impactful efforts. This role will also build tools and support structures needed to analyze, dive deep to determine root cause of system errors, network changes and performance issues. This role will need to present findings to business partners to drive improvements and prioritize customer needs to deliver the right results. Key job responsibilities · Design and implement scalable and reliable approaches to support or automate decision making throughout the business. · Apply a range of science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. · Build using statistical, mathematical, econometric, network, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. · Acquire by building the necessary / ETL queries. · Establish scalable efficient, automated processes for large scale analyses, development, validation and implementation. · Analyze for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. · Validate against alternative approaches, expected and observed outcome, and other business defined key performance indicators. · Implement that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production. · Build relationships with stakeholders and counterparts.

Do you want to join an innovative team of scientists and engineers who use terabytes of data and create state-of-the-art Generative AI algorithms to push the boundaries of AI creativity? We are building foundational behavioral models for Amazon Stores using Generative AI, LLMs and Large Model training techniques that fuses general world knowledge, customer shopping behavior and Amazon e-commerce domain knowledge. We are looking for scientists who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry using intelligent and transformative AI applications. Working closely with cross-functional teams, you will be an essential part of every stage of AI development, from ideation and design to rigorous testing and successful deployment, ensuring our AI projects drive innovation and provide value for our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities In this role you will leverage your background and expertise to lead developing foundational behavioral model for Amazon Stores using Generative AI, LLM and Large Model training techniques. On a day-to-day basis, you will: - Research and implement new algorithms and architectures for generative AI applications. - Optimize model performance and scalability for inference and deployment. - Collaborate with other talented applied scientists and engineers to gather and preprocess large datasets and develop an improved training infrastructure that accelerates innovation. - Experiment with SOTA methods to improve generative AI model quality. - Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services.

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Learn more at https://www.amazon.com/music. The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines. Key job responsibilities As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Other responsibilities include: - Collaborate with scientists, engineers, and product managers to define and frame business problems as ML or optimization tasks. - Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes - Design, development and evaluation of AI models for predictive learning - Research and implement novel machine learning and statistical approaches - Implement scalable data pipelines and model-serving systems. - Analyze experimental results, draw insights, and refine models to improve accuracy and robustness. - Communicate findings and recommendations to technical and non-technical audiences.