Careers

AWS Experience Analytics (EXA) is seeking a Research Scientist to lead customer perspectives research for the team. EXA exists to turn customer understanding into products and intelligence that teams across AWS can use. We run customer experience deep dives, product futures research, and forward-looking studies that bring decision-makers face-to-face with how customers experience AWS. The research this team produces shapes product strategy, investment decisions, and how AWS leadership thinks about the customer. What we need is someone who thinks like a scientist about customers. You have the statistical depth to work with complex behavioral data — building models, testing hypotheses, finding structure in messy signals — and the instinct to go beyond the data when the data is not enough. You are not satisfied with a model that predicts behavior without understanding why. The landscape is shifting. AWS customers are moving from traditional console-based building toward AI-augmented, agent-primary, and autonomous workflows. Understanding who these customers are, how they think, and what they need requires new research approaches — not just new data. You will design the studies, develop the frameworks, and produce the evidence that helps AWS see its customers clearly as this transformation unfolds. You will work alongside data scientists, applied scientists, engineers, and research teams who are building the data foundations for customer understanding. Key job responsibilities - Apply rigorous statistical methods to customer experience data — segmentation analysis, behavioral pattern analysis, causal inference, and outcome measurement — grounded in the team's customer lifecycle data and metrics frameworks. - Produce research findings structured to inform product strategy and leadership decisions. - Develop research frameworks and approaches for understanding emerging customer populations — AI-augmented builders, agent-primary developers, Gen Z digital natives — where existing methods may not apply. - Write compelling, clear research narratives for technical and non-technical audiences, including senior leadership. - Contribute to the team's scientific direction and mentor others. 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, has not followed a traditional path, or includes alternative experiences, do not let it stop you from applying. Mentorship and Career Growth We are continuously raising our performance bar as we strive to become Earth's Best Employer. That is why you will 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 is nothing we cannot achieve in the cloud.

The AOP (Analytics Operations and Programs) team is responsible for creating core analytics, insight generation and science capabilities for ROW Ops. We develop scalable analytics applications, AI/ML products and research models to optimize operation processes. You will work with Product Managers, Data Engineers, Data Scientists, Research Scientists, Applied Scientists and Business Intelligence Engineers using rigorous quantitative approaches to ensure high quality data/science products for our customers around the world. As a Data Scientist, you will play a crucial role in supporting the team by creating and maintaining the data infrastructure necessary for the advanced analytics and machine learning solutions. Our team solves a broad range of problems that can be scaled across ROW (Rest of the World including countries like India, Australia, Singapore, MENA and LATAM). Here is a glimpse of the problems that this team deals with on a regular basis: • Using live package and truck signals to adjust truck capacities in real-time • HOTW models for Last Mile Channel Allocation • Using LLMs to automate analytical processes and insight generation • Ops research to optimize middle mile truck routes • Working with global partner science teams to affect Reinforcement Learning based pricing models and estimating Shipments Per Route for $MM savings • Deep Learning models to synthesize attributes of addresses • Abuse detection models to reduce network losses Key job responsibilities 1. Analyze data with statistical and ML techniques. 2. Develop analysis/model in scripting languages (e.g. Python, R) and statistical/mathematical software (e.g. SAS, Matlab, etc.). 3. Develop science-based Supply Chain solutions. 4. Analysis/model documentation. 5. Learn and understand state-of-the-art statistical and ML techniques/tools. 6. Learn and understand Amazon Supply Chain operations. 7. Develop ML solutions to detect abuse in the network

The Agentic Automated Reasoning Group is pioneering the next generation of neuro-symbolic tools—fusing breakthroughs in artificial intelligence with the scale of the cloud and our deep expertise in automated reasoning. If you're driven to push the boundaries of what's possible at the intersection of learning and logic, join us and help shape this transformational initiative. The Automated Reasoning checks team is looking for a Senior Applied Scientist with experience in building scalable formal reasoning solutions that delight customers. You will be part of a world-class team building the next generation of tools and services by combining Automated Reasoning, GenAI, and Agentic AI at cloud computing scale. You will apply your knowledge to propose solutions, create software prototypes, and move prototypes into production systems using modern software development tools and methodologies. In addition, you will support and scale your solutions to meet the ever-growing demand of customer use. You will use your strong verbal and written communication skills and own the delivery of high-quality results in a fast-paced environment. Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. See https://aws.amazon.com/security/provable-security/ As a Senior Applied Scientist in the Agentic Automated Reasoning Group, you will play a pivotal role in shaping product features from beginning to end. You will: * 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 Key job responsibilities * Design and implement scalable, production-grade neuro-symbolic systems that integrate formal reasoning with GenAI to deliver reliable, verifiable outcomes for AWS customers. * Collaborate cross-functionally with product, engineering, and science teams as well as external customers to deeply understand pain points, gather requirements, and translate them into neuro-symbolic features that solve real-world problems. * Enhance and extend the capabilities of formal reasoning systems to meet the demands of GenAI and agentic applications — including areas such as hallucination detection, policy verification, and automated guardrails. * Proactively identify and pursue new opportunities to apply formal reasoning solutions across AWS services and customer domains, driving adoption and expanding the impact of neuro-symbolic approaches. * Own the end-to-end science lifecycle — from research and experimentation through production deployment — defining metrics to measure system performance and the real-world impact of neuro-symbolic solutions. * Mentor junior scientists and engineers, providing technical guidance, fostering a culture of scientific rigor, and raising the bar across the team. * Advance the state of the art through publications at top-tier venues, patents, or open-source contributions, strengthening Amazon's position as a leader in automated reasoning and neuro-symbolic AI. A day in the life As a Senior Applied Scientist on the Agentic Automated Reasoning team, you'll design and build neuro-symbolic systems that mathematically verify AI-generated policy content. Day to day, you'll run experiments and invent features to improve Automated Reasoning checks in Amazon Bedrock Guardrails, collaborate with engineering and product teams to ship features into production, and partner with other AWS agentic AI teams to integrate neuro-symbolic reasoning into workflows. You'll engage directly with customers in regulated industries to translate real-world policy challenges into research priorities, while mentoring junior scientists and publishing at top-tier venues. About the team You will be working with a team of formal methods and machine learning specialists spanning recently hired PhDs to industry veterans. You will work collaboratively to deliver results in the form of new features for Automated Reasoning checks that delight our customers. 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 ‘s Tax engine organisation is looking for a passionate and innovative science leader to take its science initiatives to new heights. Amazon Tax Engine platform backs all of the orders placed across Amazon e-commerce. We serve Amazon customers and sellers by correctly computing and collecting the tax amounts when an Amazon order is placed globally. We are responsible for correctly attributing products to the correct Tax categories applicable for the specific country, state and county, providing core calculation services that calculate taxes (sales tax and VAT) for all Amazon sales, physical and digital. Our challenges include staying on top of the complex and ever-changing global tax legislations as well as computing calculations correctly and quickly, thousands of times a second, with accuracy close to 100%. We use language models at scale for Tax classification of the diverse products in Amazon catalogue We have a growing portfolio of science problems that includes balance of predictive and generative AI -including language comprehension, causal reasoning and active learning. Key job responsibilities - Manage and mentor a talented team of senior Appleid Scientists and SDEs. - Improve process and methodologies pertaining to deliverables of the team. - Strike the right balance between experimentation and delivery for sustained impact and long term gains. - Partner with stakeholders and customers to build roadmap for new products and services.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. We are seeking a technical leader for our Search Thematic Advertising Experiences team to lead a multi-disciplinary team of science and engineering. This team is within the Sponsored Product team, and works on complex engineering, optimization, econometric, and user-experience problems in order to deliver relevant product ads on Amazon search and detail pages world-wide. The team operates with the dual objective of enhancing the experience of Amazon shoppers and enabling the monetization of our online and mobile page properties. Our work spans ML and Data science across predictive modeling, reinforcement learning (Bandits), adaptive experimentation, causal inference, data engineering. Key job responsibilities Search Thematic Advertising Experiences , within Sponsored Products, is seeking an Applied Science Manager to join a fast growing team with the mandate of creating new ads experience that elevates the shopping experience for our hundreds of millions customers worldwide. We are looking for a top analytical mind capable of understanding our complex ecosystem of advertisers participating in a pay-per-click model– and leveraging this knowledge to help turn the flywheel of the business. As an Applied Science Manager on this team you will: --Lead a multi-disciplinary team of applied scientists and engineers. --Act as the technical leader in Machine Learning and drive full life-cycle Machine Learning projects. --Lead technical efforts within this team and across other teams. --Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production. --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. --Work closely with software engineers to assist in productionizing your ML models. --Research new machine learning approaches. --Recruit Applied Scientists to the team and act as a mentor to other scientists on the team. A day in the life The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and with an ability to work in a fast-paced, high-energy and ever-changing environment. The drive and capability to shape the direction is a must. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to customers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon

Do you want to help shape the future of Amazon's physical retail presence? Worldwide Grocery Stores (WWGS), Location Strategy and Analytics team is looking for a Sr. Applied Scientist to join us in developing advanced forecasting models, optimization models, and analytical tools to support critical real estate and network planning decisions for Amazon's Worldwide Grocery business, including Whole Foods Market. Our team is responsible for developing predictive models and tools to support Real Estate and Topology analysts in making important decisions regarding our stores—including new store openings, relocations, closures, remodels, design, new formats, and more. We leverage statistical modeling, machine learning, and GenAI to build solutions for store sales forecasting, sales transfer effects, macrospace optimization, store network optimization, store network diffusion planning, and causal effects. As a Sr. Applied Scientist on our team, you will apply your deep technical expertise to tackle complex business problems and develop innovative solutions to improve our forecasting, decision-making capabilities, and MLOps. You will collaborate with a diverse team of scientists, economists, and business partners to identify opportunities, develop hypotheses, build internal products, and translate analytical insights into actionable recommendations for Executive Leadership. Key job responsibilities - Design and implement forecasting models and machine learning solutions to predict store performance and optimize our retail network. - Analyze large datasets to uncover insights and patterns related to store performance, customer behavior, and market dynamics. - Develop and own end-to-end solutions, tools and frameworks to scale our ML model development, MLOps, and data analysis. - Leverage GenAI models to enhance user interaction with our solutions, improve overall user experience, and build new features. - Present research findings and recommendations to scientists, business leaders, and executives. - Collaborate with cross-functional teams to drive adoption of models and insights. - Mentor junior scientists, providing technical guidance and supporting their professional growth. - Stay current on latest developments in relevant fields and propose innovative approaches. About the team We are a team of scientists passionate about leveraging data and advanced analytics to drive strategic decisions for Amazon's grocery business. Our work directly impacts Amazon's worldwide grocery store growth and development strategy. We foster a collaborative environment where team members are encouraged to think creatively, challenge assumptions, and pursue novel approaches to solving complex problems. Our team is at the forefront of applying a multitude of techniques - including GenAI - to improve our scientific solutions and products.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. As the Applied Science Manager for the Continuous Model Evaluation and Learning workstream, you will own the quality backbone for this agentic brand-intelligence system. You will lead a mix of applied scientists and engineers who define what "good" looks like for each brand skill, instrument the system to measure it, diagnose why skills underperform, and close the loop by generating, validating, and deploying improvements. You will deliver the evaluation and remediation framework that attains accuracy targets, enables forward evaluation for skills as they develop, and establishes the autonomous detect-diagnose-remediate loop that lets us scale quality across all brand skills and multiple advertiser-facing surfaces. This is a business-critical, greenfield initiative within SPB. You will set the scientific charter, grow the talent on your team, and ship the framework that every other brand-intelligence workstream depends on. Key job responsibilities - Lead, mentor, and grow the talent on a team composed of applied scientists and machine learning engineers, fostering a culture of scientific excellence, customer obsession, and ownership. - Own the scientific vision and multi-quarter roadmap for continuous model evaluation and learning across the brand-intelligence system. - Design and deliver evaluation frameworks for agentic brand-intelligence skills, including LLM-as-Judge rubrics, multi-model ensemble judging, gold-set construction, and calibration against human evaluators. - Lead development of the optimization engine that programmatically refines prompts, generates synthetic training pairs, and composes agent decomposition strategies (orchestrator-worker patterns) when single-agent skills hit complexity limits. - Establish rigorous offline-to-online consistency, A/B testing discipline, and drift monitoring so that quality improvements generalize to production traffic. - Communicate scientific vision, research breakthroughs, and business outcomes to senior leadership, and drive alignment with broader Amazon Advertising objectives.

The Supply Chain Optimization Technologies (SCOT) team builds technology to automate and optimize Amazon’s supply chain of physical goods. We seek a Data Scientist with strong analytical and communication skills to join our team. SCOT manages Amazon's inventory under uncertainty of demand, pricing, promotions, supply, vendor lead times, and product life cycle. We optimize complex trade-offs between customer experience, inventory costs, fulfillment costs, fulfillment center capacity, etc. We develop sophisticated algorithms that involve learning from large amounts of data such as prices, promotions, similar products, and other data from our product catalog in order to automatically act on millions of dollars’ worth of inventory weekly and establish plans for tens of thousands of employees. As a Data Scientist, you will contribute to the research community, by working with other scientists across Amazon and our Supply Chain, as well as collaborating with academic researchers and publishing papers both internally and externally. Key job responsibilities Major responsibilities include: - Analysis of large amounts of data from different parts of the supply chain and their associated business functions - Improving upon existing machine learning methodologies by developing new data sources, developing and testing model enhancements, running computational experiments, and fine-tuning model parameters for new models - Formalizing assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them - Communicating verbally and in writing to business customers with various levels of technical knowledge, educating them about our research, as well as sharing insights and recommendations - Utilizing code (Python, R, Scala, etc.) for analyzing data and building statistical and machine learning models and algorithms A day in the life As a Data Scientist in SCOT, you will be tasked to understand and work with cutting edge research to enable the implementation of sophisticated models on big data. As a successful data scientist in the SCOT team, you are an analytical problem solver who enjoys diving into data from various businesses, is excited about investigations and algorithms, can multi-task, and can credibly interface between scientists, engineers and business stakeholders. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future.

Are you excited about leveraging state-of-the-art Deep Learning, Recommender Systems, Information Retrieval, Natural Language Processing algorithms on large datasets to solve real-world problems? As an Applied Scientist Intern, you will be working in the closest Amazon offices to you (Sydney, Melbourne, Adelaide, Brisbane) in a fast-paced, cross-disciplinary team of experienced R&D scientists. You will take on complex problems, work on solutions that leverage existing academic and industrial research, and utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even deliver these to production in customer facing products. Key job responsibilities - Develop novel solutions and build prototypes - Work on complex problems in Machine Learning and Information Retrieval - Contribute to research that could significantly impact Amazon operations - Collaborate with a diverse team of experts in a fast-paced environment - Collaborate with scientists on writing and submitting papers to top conferences, e.g. NeurIPS, ICML, KDD, SIGIR - Present your research findings to both technical and non-technical audiences Key Opportunities: - Work in a team of ML scientists to solve recommender systems problems at the scale of Amazon - Access to Amazon services and hardware - Become a disruptor, innovator, and problem solver in the field of information retrieval and recommender systems - Potentially deliver solutions to production in customer-facing applications - Opportunities to be hired full-time after the internship Join us in shaping the future of AI at Amazon. Apply now and turn your research into real-world solutions!

AWS Experience Analytics (EXA) is seeking an Applied Scientist to join our team. EXA exists to turn customer understanding into products and intelligence that teams across AWS can use. We are building a unified customer lifecycle data platform, customer experience measurement frameworks, and segmentation systems, and the science that powers these products is well underway. What we need is someone who can add to our work in signal analysis, pattern discovery, and predictive modelling — bringing both scientific depth and the production engineering skills to take models from notebook to production. You will bring your creative and learn and be curious mindset and work within the science team helping us ship faster across the full range of modelling and ML work and at greater scale. The problems are genuinely interesting. AWS customers are shifting from console-based building toward AI-augmented, agent-primary, and autonomous workflows. The signals that tell us who customers are, what they are trying to do, and where they struggle are changing fundamentally. There is more to model, more to explore, and more to build than the current team can get to — and that is where you come in. Key job responsibilities - Contribute to and extend the team's work in signal analysis, pattern discovery, and predictive modelling — adding scientific depth and production engineering capability. - Build production ML infrastructure — offline training pipelines, online scoring systems, and monitoring. - Frame and tackle new modelling problems as they emerge — particularly around behavioral signals from AI agents and agentic workflows. - Extend and invent scientific techniques where needed, while also knowing when existing approaches are sufficient, and speed matters more than novelty. - Collaborate with engineers building the CLARA platform, the Experience Metrics Framework, and the Customer Segmentation Framework to ensure ML systems integrate cleanly and serve the broader product vision. - Contribute to the team's scientific direction — proposing new modelling initiatives, sharing approaches, and helping the team make good trade-offs between rigor and velocity. - Mentor others and contribute to the broader applied science community. - Write clear technical documentation describing your approaches, trade-offs, and results. 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, has not followed a traditional path, or includes alternative experiences, do not let it stop you from applying. Mentorship and Career Growth We are continuously raising our performance bar as we strive to become Earth's Best Employer. That is why you will 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 is nothing we cannot achieve in the cloud.