Careers

This role is on the Core Tech Private Brands Analytics (PBA) team, a cross-functional team (software engineering, data science, data engineering, business intelligence) that owns Amazon Private Brands (APBs) central data infrastructure and builds platforms and models that help improve business performance. In this job you will build and improve forecasting and planning models across APB, partnering with business, science, and tech stakeholders. Day-to-day work includes end-to-end pipeline development (feature engineering through training and deployment) on SageMaker, S3, and Datanet, replacing manual spreadsheet-driven processes with reproducible code-driven pipelines and dashboards, evaluating model accuracy across business segments, and contributing to APB's science standards alongside a senior scientist assessing the org's AI framework and experimentation rigor. Key job responsibilities The ideal candidate has strong fundamentals in forecasting and applied ML, experience with Python and SQL, comfort working with large-scale retail datasets, and the ability to communicate findings clearly to non-technical partners.

As part of IRR (Inventory Routing and Replenishment) organization within SCOT-Inbound systems, Applied Scientists own algorithms powering inventory routing, replenishment, and modeling / simulation of Amazon's fulfillment network utilizing optimization and machine learning toolsets. We are looking for a talented applied scientist with a passion for designing and implementing efficient and elegant scientific solutions for Amazon-scale supply chain problems. Key job responsibilities - Design and develop advanced mathematical optimization and machine learning solutions in the domains of inventory optimization, distribution optimization, network design, and control theory. - Use methods in learned and model-based online and offline control techniques and algorithms to design efficient exact or heuristic solution methodologies to be used by in-house decision support tools and software. - Research, prototype, simulate, and experiment with these models using programming languages such as Java and Python; participate in the production level deployment. - Closely work with software engineering teams and write well-tested production Java/Python code for science modules within engineering-managed services. Provide time-sensitive on-call support and high-severity issue support when bugs are identified in production code. Improve code quality of legacy scientific production code. - Create, enhance, and maintain technical documentation and science designs. - Present to other Scientists, Product, and Software Engineering teams, as well as Stakeholders. - Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans. - Influence organization's long-term roadmap and resourcing, onboard new technologies onto Science team's toolbox, mentor other Scientists. A day in the life - Engage with customers to understand their problems. - Collaborate with product partners and peers to design and deliver algorithmic solutions to these problems. - Implement these solutions in java within engineering systems through close collaboration with engineering partners achieving high code quality. - Deploy and measure impact of implementations. - Support customers and stakeholders whenever deep-dives and enhancements are needed as they relate to scientific products the team owns. - Contribute to product roadmap through new innovations on behalf of customers. - Publish work in internal and external scientific community. Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: Medical, Dental, and Vision Coverage Maternity and Parental Leave Options Paid Time Off (PTO) 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team IRR Science team under SCOT Inbound Systems is comprised of applied scientists with strong optimization & ML science depth and object-oriented programming & design patterns knowledge. Given the scale of problems we solve for our customers and mission-critical nature of our solutions, systems thinking driven approach, with attention to algorithmic complexity, solution quality, simplicity, and extensibility are of critical importance. We collaborate with engineering teams closely and prioritize solving problems with minimally complex solutions while maintaining quality. We build solutions that must consistently improve customer experience with maximum transparency and explainability of decisions made by such solutions. We strive for every member of the team to be knowledgeable about every product that the team owns to enable meaningful collaboration within the team. We seek to publish our work at internal and external scientific communities when they produce novel solutions.

We are expanding our Global Risk Management & Claims team and insurance program support for Amazon’s growing risk portfolio. This role will partner with a wide range of stakeholders to build underwriting and claims models, determine rate and reserve adequacy, build cloud-based modeling tools, and provide other analytical support for financially prudent decision making. As a member of the Global Risk Management team, this role will provide actuarial and data science support for Amazon’s worldwide operation. Key job responsibilities ● Collaborate with risk management and claims team to identify insurance gaps, propose solutions, and measure impacts insurance brings to the business ● Develop models for new and existing insurance programs utilizing actuarial and data science techniques in innovative ways ● Build forecasts and analyses for businesses under rapid growth, including trend studies, loss distribution analysis, ILF development, and industry benchmarks ● Create processes to monitor loss cost and trends ● Propose and implement loss prevention initiatives with impact on insurance costs in mind ● Advise underwriting decisions with analysis on exposure risk profile ● Support insurance cost budgeting activities ● Collaborate with external vendors and other internal science teams to extract insurance insight ● Conduct other ad hoc analyses and risk modeling as needed

Passionate about books? The Amazon Books team is looking for a talented Applied Scientist II to help invent, design, and deliver science solutions to make it easier for millions of customers to find the next book they will love. In this role, you will - Be a part of a growing team of scientists, economists, engineers, analysts, and business partners. - Use Amazon’s large-scale computing and data resources to generate deep understandings of our customers and products. - Build highly accurate models (and/or agentic systems) to enhance the book reading & discovery experiences. - Design, implement, and deliver novel solutions to some of Amazon’s oldest problems. Key job responsibilities - Inspect science initiatives across Amazon to identify opportunities for application and scaling within book reading and discovery experiences. - Participate in team design, scoping, and prioritization discussions while mapping business goals to scientific problems and aligning business metrics with technical metrics. - Spearhead the design and implementation of new features through thorough research and collaboration with cross-functional teams. - Initiate the design, development, execution, and implementation of project components with input and guidance from team members. - Work with Software Development Engineers (SDEs) to deliver production-ready solutions that benefit customers and business operations. - Invent, refine, and develop solutions to ensure they meet customer needs and team objectives. - Demonstrate ability to use reasonable assumptions, data analysis, and customer requirements to solve complex problems. - Write secure, stable, testable, and maintainable code with minimal defects while taking full responsibility for your components. - Possess strong understanding of data structures, algorithms, model evaluation techniques, performance optimization, and trade-off analysis. - Follow engineering and scientific method best practices, including design reviews, model validation, and comprehensive testing. - Maintain current knowledge of research trends in your field and apply rigorous scrutiny to results and methodologies. A day in the life In this role, you will address complex Books customer challenges by developing innovative solutions that leverage the advancements in science. Working alongside a talented team of scientists, you will conduct research and execute experiments designed to enhance the Books reading and shopping experience. Your responsibilities will encompass close collaboration with cross-functional partner teams, including engineering, product management, and fellow scientists, to ensure optimal data quality, robust model development, and successful productionization of scientific solutions. Additionally, you will provide mentorship to other scientists, conduct reviews of their work, and contribute to the development of team roadmaps. About the team The team consists of a collaborative group of scientists, product leaders, and dedicated engineering teams. We work with multiple partner teams to leverage our systems to drive a diverse array of customer experiences, owned both by ourselves and others, that enable shoppers to easily find their perfect next read and enable delightful reading experiences that would make Kindle the best place to read.

Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As a STRUC Economist Intern, you'll specialize in structural econometric analysis to estimate fundamental preferences and strategic effects in complex business environments. Your responsibilities include: Analyze large-scale datasets using structural econometric techniques to solve complex business challenges Applying discrete choice models and methods, including logistic regression family models (such as BLP, nested logit) and models with alternative distributional assumptions Utilizing advanced structural methods including dynamic models of customer or firm decisions over time, applied game theory (entry and exit of firms), auction models, and labor market models Building datasets and performing data analysis at scale Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations Tackling diverse challenges including pricing analysis, competition modeling, strategic behavior estimation, contract design, and marketing strategy optimization Helping business partners formalize and estimate business objectives to drive optimal decision-making and customer value Build and refine comprehensive datasets for in-depth structural economic analysis Present complex analytical findings to business leaders and stakeholders

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 Selling Partner Trust & Store Integrity Science Team. We are looking for a talented scientist who is passionate to build advanced machine learning systems that help manage the safety of millions of transactions every day and scale up our operation with automation. Key job responsibilities Innovate with the latest GenAI/LLM/VLM technology to build highly automated solutions for efficient risk evaluation and automated operations Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to create impactful business value Learn, explore and experiment with the latest machine learning advancements to create the best customer experience A day in the life You will be working within a dynamic, diverse, and supportive group of scientists who share your passion for innovation and excellence. You'll be working closely with business partners and engineering teams to create end-to-end scalable machine learning solutions that address real-world problems. You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation. You will also be providing clear and compelling reports for your solutions and contributing to the ongoing innovation and knowledge-sharing that are central to the team's success.

Every day, hundreds of thousands of Amazon associates show up to fulfill the promise we make to our customers. Behind the workforce decisions that support them — staffing, retention, scheduling, development — there should be science that doesn't just describe what happened, but explains why it happened and predicts what comes next. That's the work we do. PXT Central Science (PXTCS) is Amazon's internal research organization dedicated to bringing scientific rigor to people and workforce decisions at global scale. Our team sits within the part of PXTCS that focuses on Amazon's Tier 1 hourly populations — the associates at the heart of Amazon's operations. We are a multidisciplinary group of 15 economists, data scientists, data engineers, and research scientists united by a single mission: to transform complex operational challenges into actionable insights through rigorous causal analysis and predictive modeling that empowers data-driven workforce decisions. We are building something new — causal predictive models that go beyond traditional forecasting. Our models don't just tell leaders what will happen; they reveal why it will happen and what levers they can pull to change the outcome. This is the frontier where causal inference meets modern machine learning, and we need a scientist who can help us push it forward. As a Senior Applied Scientist on this team, you will be the connective tissue between innovative research and real-world impact. You will work shoulder-to-shoulder with economists who deeply understand the causal mechanisms driving workforce dynamics and data scientists who know the operational landscape — and you will bring the technical creativity to expand what's possible. That means writing production-quality code that our partner engineering teams can implement into decision-making tools. It means exploring novel feature spaces — large language models, computer vision, and other emerging techniques — to unlock signal that traditional approaches miss. And it means doing all of this with the scientific rigor that causal claims demand. This role is built for someone who is entrepreneurial and energized by ambiguity — someone who sees a prototype model and immediately starts thinking about how to make it robust, scalable, and impactful. You will not just advance your own work; you will elevate the scientists around you. We are looking for a strong technical individual contributor who is passionate about developing peers, raising the bar across disciplines, and who sees a future path into a science manager position. If you want to do science that directly shapes how Amazon supports its workforce — not in theory, but in production systems that leaders use to make better decisions every day — we'd love to talk. Key job responsibilities Design and build causal predictive models that move beyond correlation — developing systems that forecast workforce outcomes and identify the actionable drivers behind them, enabling leaders to intervene before problems materialize Pioneer novel feature engineering by bringing creative approaches from LLMs, computer vision, and other emerging techniques into the causal modeling pipeline, unlocking signal that traditional econometric and tabular methods miss Write production-quality science code that your partner engineering team can implement directly into operational decision-making tools — your work must be clean, well-documented, and built to scale Bridge disciplines by translating between economists, data scientists, and engineers — synthesizing causal rigor with ML innovation to produce models that are both scientifically defensible and operationally useful Design and execute experiments to validate causal claims and model performance, establishing evaluation standards that the team and stakeholders trust Develop and elevate peers across the team — mentoring scientists in adjacent disciplines, sharing technical knowledge, and raising the collective bar on modeling and engineering practices Present findings to senior leadership, distilling complex causal and predictive insights into clear recommendations that drive workforce strategy for Amazon's Tier 1 hourly populations. About the team Amazon’s People Experience and Technology Central Science (PXTCS) team uses economics, behavioral science, statistics, machine learning, applied science, and Generative AI to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science, engineering, and UX to develop and deliver solutions that measurably achieve this goal.

At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities Key job responsibilities include: * Formulate open research problems at the intersection of GenAI, multimodal reasoning, and large-scale information retrieval—defining the scientific questions that transform ambiguous, real-world catalog challenges into publishable, high-impact research * Push the boundaries of VLMs, foundation models, and agentic architectures by designing novel approaches to product identity, relationship inference, and catalog understanding—where the problem complexity (billions of products, multimodal signals, inherent ambiguity) demands methods that don't yet exist * Advance the science of efficient model deployment—developing distillation, compression, and LLM/VLM serving optimization strategies that preserve frontier-level multimodal reasoning in compact, production-grade architectures while dramatically reducing latency, cost, and infrastructure footprint at billion-product scale * Make frontier models reliable—advancing uncertainty calibration, confidence estimation, and interpretability methods so that frontier-scale GenAI systems can be trusted for autonomous catalog decisions impacting millions of customers daily * Own the full research lifecycle from problem formulation through production deployment—designing rigorous experiments over petabytes of multimodal data, iterating on ideas rapidly, and seeing your research directly improve the shopping experience for hundreds of millions of customers * Shape the team's research vision by defining technical roadmaps that balance foundational scientific inquiry with measurable product impact * Mentor scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building deep organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research

Project Leo (former Kuiper) is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Systems Engineer, this role is primarily responsible for the design, development and integration of communication payload and customer terminal systems. The Role: Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology at global scale. The team develops and designs the communication system for project Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in designing and developing advanced technology and solutions for communication system. This role will also be responsible developing advanced physical layer + protocol stacks systems as proof of concept and reference implementation to improve the performance and reliability of the LEO network. In particular this role will be responsible for using concepts from digital signal processing, information theory, wireless communications to develop novel solutions for achieving ultra-high performance LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. This role will also play a critical role in the integration and verification of various HW and SW sub-systems as a part of system integration and link bring-up and verification. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

As a Data Scientist in Alexa Connections, you will lead the end-to-end development of machine learning and data science solutions that power intelligent communication experiences across channels such as calling, messaging and email. You will partner closely with product, engineering, and business leaders to translate ambiguous problems into scalable ML models, experimentation frameworks, and data-driven product decisions. In this role, you will design and deploy advanced ML and statistical models for capabilities such as prioritization, intent detection, and proactive action recommendations. You will analyze large-scale datasets and run rigorous experiments, including A/B testing and causal analysis, to measure impact and continuously improve customer engagement and product performance. Additionally, you will shape the applied science roadmap and collaborate with global cross-functional teams to deliver AI-driven solutions that scale to millions of Alexa customers. Key job responsibilities - Partner with product, engineering, operations, and security teams to translate complex business problems into scalable, production-ready data science and ML solutions. - Own the full lifecycle of model development, including exploratory analysis, model design, deployment, monitoring, and continuous improvement. - Define and track success metrics, conduct rigorous analyses, and provide insights that guide product launches, feature improvements, and data-driven decision-making. - Build data-driven business cases to prioritize science initiatives and demonstrate measurable impact of ML solutions. - Develop ML-powered systems supporting key business areas. - Lead research and analysis to understand customer interactions with Alexa, and enhance overall customer experience. - Contribute to the broader science community by mentoring analysts, improving data workflows and tooling, and publishing technical work in internal and external forums. A day in the life • Deep dive into our business metrics, analyze data, trends, and reviewing dashboards • Writing code: building packages in Python, writing SQL queries, deploying solutions for Connections experience teams to consume. • Leading or joining working sessions with Product Managers to refine problem statements new initiatives. • Exploring new features and model architectures, leveraging AWS services, documentation, and upskilling yourself to the latest technologies. • Leverage pre-trained LLMs to build applications that solve business problems for Connections experiences. • Meet with Sr. Engineers/Principal Engineers to align on solution designs. • Own or co-own MBR, WBR documents that are reviewed with Connections leadership team.