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Amazon Economics is seeking Structural IO Economist (STRUC) Interns who are passionate about applying structural econometric methods to solve real-world business challenges. STRUC economists specialize in the econometric analysis of models that involve the estimation of fundamental preferences and strategic effects. In this full-time internship (40 hours per week, with hourly compensation), you'll work with large-scale datasets to model strategic decision-making and inform business optimization, gaining hands-on experience that's directly applicable to dissertation writing and future career placement. By applying to this role, you are automatically being considered for all our available STRUC internships in 2027. Key job responsibilities 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

The People eXperience Technology (PXT) Central Science (PXTCS)'s mission is to make PXT the most scientific, technologically proficient, and inclusive HR organization in the world. PXTCS does this by accelerating scientific rigor in business-led initiatives, working backwards from employee experience, and delivering science-driven products that improve the well-being of and value of work for Amazonians worldwide. We are seeking an Applied Scientist to build production machine learning systems that solve complex business problems at scale. You will design, develop, and deploy ML solutions that directly impact millions of users and drive strategic decision-making across the organization. In this role, you will work on challenging problems spanning predictive modeling, computer vision, natural language processing, recommendation systems, and generative AI applications. You will collaborate with cross-functional teams to translate ambiguous business challenges into rigorous technical solutions. Key job responsibilities - Apply and adapt state-of-the-art scientific techniques to solve well-defined problems in employee experience, using reasonable assumptions, data, and customer requirements. - Design, develop, and implement small-to-medium ML components with input and guidance from senior scientists, taking ownership of the code in your components. - Write secure, stable, testable, maintainable, well-reviewed code (at the SDE I bar) to deliver solutions into production that benefit customers and the business. - Collaborate with cross-functional partners to understand business context and impact, and help mentor interns. - Communicate complex technical concepts to diverse audiences, from technical peers to senior leadership About the team The People eXperience and Technology Central Science Team (PXTCS) uses economics, applied science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.

Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists developing models to model customer behavior and optimize the customer experience with Amazon Prime. This includes understanding who our customers are, long-term value of the Prime membership program, and creating the right personalized framework for content and subscription optimization. As an AI/ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions. There are numerous scientific and technical challenges you will get to tackle in this role, such as optimizing/fine-tuning GenAI/LLM solutions for Prime personalization, building GenAI foundation models, global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from GenAI/LLMs, supervised/semi-supervised learning, deep learning, transformer architectures, using outcomes from causal Econometric modeling, and Reinforcement learning. As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of. You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Sagemaker, DynamoDB, S3, ClaudeCode), various AI/ML algorithms and techniques (Deep Learning, GenAI/LLMs, transformers, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques. Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. Develop offline policy estimation tools and integrate with measurement systems/econometric models. Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems. Key job responsibilities Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. Develop offline policy estimation tools and integrate with measurement systems/econometric models. Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.

Are you fascinated by the power of Natural Language Processing (NLP) and Large Language Models (LLM) to transform the way we interact with technology? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the e-commerce space? If so, Amazon's International Seller Services team has an exciting opportunity for you as an Applied Scientist. At Amazon, we strive to be Earth's most customer-centric company, where customers can find and discover anything they want to buy online. Our International Seller Services team plays a pivotal role in expanding the reach of our marketplace to sellers worldwide, ensuring customers have access to a vast selection of products. As an Applied Scientist, you will join a talented and collaborative team that is dedicated to driving innovation and delivering exceptional experiences for our customers and sellers. You will be part of a global team that is focused on acquiring new merchants from around the world to sell on Amazon’s global marketplaces around the world. The position is based in Seattle but will interact with global leaders and teams in Europe, Japan, China, Australia, and other regions. Join us at the Central Science Team of Amazon's International Seller Services and become part of a global team that is redefining the future of e-commerce. With access to vast amounts of data, emerging technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way sellers engage with our platform and customers worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language-related challenges in the international seller services domain. Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. Continuously explore and evaluate state-of-the-art NLP techniques and methodologies to improve the accuracy and efficiency of language-related systems. Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. A day in the life Push the boundaries of applied science - fine-tune large language models and develop novel NLP techniques to crack complex challenges in seller acquisition, content generation, and catalog understanding Work with data at massive scale - tap into some of the richest e-commerce datasets in the world to uncover patterns, generate insights, and drive real business impact Turn research into reality - prototype bold ideas, then partner with engineers to bring your models into production, balancing scientific rigor with real-world scalability Think globally, deliver worldwide - collaborate with leaders and teams across Europe, Japan, China, and Australia to build solutions that generalize across international marketplaces Solve problems that matter - translate ambiguous business challenges into well-scoped science problems that directly serve sellers and customers around the globe Collaborate with the best - engage in design reviews, contribute to technical thought leadership, and learn from a diverse community of world-class scientists and engineers Never stop learning - stay at the frontier of NLP, LLMs, and applied ML, bringing the latest research advances into your work Own your impact - operate with a customer-obsessed mindset where every model you build helps sellers thrive and expands selection for customers worldwide

AWS Startups supports hundreds of thousands of founders globally — from first credit to scaled production workload. Data and machine learning are how we identify high-potential startups early, personalize the guidance we deliver, and decide where to invest next. We recently launched AWS Startup Advisor, an AI-powered service that brings AWS expertise directly into the developer tools founders already use, and we need a scientist to build the ML capabilities that power its proactive recommendations. In this role, you will own science problems end-to-end — from the data foundation that unifies signals about founders and their products, through model design and evaluation, to production deployment serving hundreds of thousands of startups. If you want your models to reach real founders the same week you ship them, this is the role. Key job responsibilities - Own the science for problem areas end-to-end: frame the problem, define the data strategy, build and evaluate ML models for recommendation systems, startup segmentation, and fraud detection, and deploy them into production. - Apply generative AI and large language models to personalize the technical guidance founders receive, including retrieval, ranking, and evaluation of LLM-powered experiences. - Design and run experiments that keep model quality quantified and defensible, making clear trade-offs between accuracy, latency, and cost as you balance rapid iteration with production reliability. - Partner with product, engineering, design, and go-to-market teams to translate science into scalable products, and communicate results clearly to both technical and non-technical leaders. - Raise the scientific bar through design and code reviews, mentor other scientists and engineers, and contribute to the broader scientific community through publications or peer reviews. A day in the life You might start the morning digging into a messy dataset to uncover a new segmentation signal, then shift to reviewing an A/B test that measures how a recommendation model is changing founder engagement. After lunch you could pair with an engineer to optimize inference latency for a fraud-detection model, then join a product review where you present trade-offs between two ranking approaches for AWS Startup Advisor. Expect to regularly move between hands-on modeling work and cross-team conversations that shape what gets built next. About the team The AWS Startups team builds products and platforms that support startup customers at every stage of their journey — from onboarding and credit programs to AI-powered guidance and scale solutions. We partner with business development, field marketing, and solutions architecture teams worldwide, and our portfolio serves hundreds of thousands of startups globally. We are building the next generation of AI-native products that make world-class cloud expertise accessible to every founder, and you will help shape the scientific direction that gets us there.

Do you ever struggle to explain your work? How's this: "Do you shop at Amazon? Do you know that box that says 'Add to Cart' and shows a price? Our team owns the model which picks that offer and the customer experience around the display of the offer price and the elements surrounding the 'Add to Cart' button which inform a purchase decision. We pick and display offers several billion times a day across all surfaces (mobile app, mobile web, desktop, Alexa shopping) worldwide." The mission of the Amazon Buying Experience organization is to be the world’s first and most trusted choice for every customer on earth to discover and evaluate any product or service. Our team blends machine learning models to rank and select the best offer from the most trusted merchant for all products sold on Amazon along with a world-class front-end user experience for offer comparison to our global customers. We are responsible for the experiences and services that enable developers, including our own, to create tailored shopping experiences for every customer, product, business and marketplace offered by Amazon. We build scalable and extensible frameworks which allow for many teams at Amazon to innovate within the Offers Experience in a federated manner. If you are passionate about influencing and delivering the next-generation Amazon customer buying experience, we want to meet you. We are looking for an Economist to join one of the most impactful and visible teams in Amazon. In this role you will work with business stakeholders throughout Amazon and provide technical direction and business expertise to strategize and help launch new businesses and features for our customers. You will use data to justify customer friendly decisions and present customer and financial impact of the changes we make to our stakeholders. You will work closely with other economists, engineers, product managers, applied scientists, TPMs, managers, and senior leadership team to understand and drive business impact. Successful candidates will have experience working on multiple projects with different stakeholders, make data driven decisions, have strong communication skills to interact with executives, non-technical and technical individuals and have a high technical bar along with a passion for people and project management. This is an opportunity to work with a team that drives one of the most coveted real estate in the E-commerce, the Amazon ‘Buy Box’ on Amazon Product Detail Page, Amazon Search Page , multiple buying widgets etc. on the Amazon desktop, mobile and tablet environments. Key job responsibilities Today, millions of third party sellers offer products alongside Amazon Retail though the Amazon Marketplace and compete to be featured on the ‘Add to Cart’ button (aka the ‘Buy Box’) on Amazon’s most valuable retail real estate – the Product Detail Page referenced above. The Featured Merchant Algorithm (FMA) teams owns the Tier – I systems and Machine Learning algorithms that selects the offer to be featured on the Buy Box and on Amazon Search Pages. The team’s mission is to ensure that the featured offer provides the best possible customer value based on factors including price, availability, delivery options and customer service. The Amazon ‘Buy Box’ is arguably the most valued real estate in the E-commerce world and we are looking for strong leaders to continue innovating for this customer facing functionality. We are looking for an Economist to join one of the most impactful and visible teams in Amazon. In this role you will work with business stakeholders throughout Amazon and provide technical direction and business expertise to strategize and help launch new businesses and features for our customers. You will use data to justify customer friendly decisions and present customer and financial impact of the changes we make to our stakeholders. You will work closely with other economists, engineers, product managers, applied scientists, TPMs, managers, and senior leadership team to understand and drive business impact.

Want to help Amazon tell its customer-centric story around the world and work in a highly cross-functional environment with economists, lawyers, scientists, public policy, public relations, and business teams? If yes, keep reading! You'll join a team of economists, engineers, and lawyers to develop economic analysis and evidence supporting legal and regulatory matters across all our lines of business worldwide—including retail, marketplace services, AWS, consumer experience, shopping and search, and operations. In this role, you will have exposure to complex regulatory issues that are of high strategic importance to the company and will develop significant expertise on the economics of Amazon’s business operations and the industries in which it operates. If you're an economist with a passion for the current legal and policy debate, strong practical judgment and creative problem-solving skills, a love of communicating economic ideas to non-technical audiences, a knack for distilling data and economic models into key insights, and a track record of delivering results fast, we want to talk to you! Key job responsibilities • Provide data-driven guidance on high-stakes legal and regulatory questions facing Amazon worldwide • Collaborate with economists, scientists, engineers, and non-technical partners on high-impact projects with global scope • Partner with global public policy teams to apply economic analyses to current policy debates on competition, AI, and related issues • Engage with external stakeholders to drive deeper understanding of Amazon’s business model and the value it develops for the economy • Support requests for economic analyses and data in ongoing regulatory and litigation matters worldwide • Synthesize business facts and data into compelling economic narratives, translating complex findings into actionable insights • Advise stakeholders across Amazon on a broad spectrum of complex and often novel economic issues • Conduct, direct, and coordinate all phases of research projects—defining key questions, evaluating methodology, executing analysis, and communicating results

Amazon Economics is seeking Reduced Form Causal Analysis (RFCA) Economist Interns who are passionate about applying econometric methods to solve real-world business challenges. RFCA represents the largest group of economists at Amazon, and these core econometric methods are fundamental to economic analysis across the company. In this a full-time internship (40 hours per week, with hourly compensation). You'll work with large-scale datasets to analyze causal relationships and inform strategic business decisions, gaining hands-on experience that's directly applicable to dissertation writing and future career placement. By applying to this role, you are automatically being considered for all our available RFCA internships in 2027. Key job responsibilities As an RFCA Economist Intern, you'll specialize in econometric analysis to determine causal relationships in complex business environments. Your responsibilities include: - Analyze large-scale datasets using advanced econometric techniques to solve complex business challenges - Applying econometric techniques such as regression analysis, binary variable models, cross-section and panel data analysis, instrumental variables, and treatment effects estimation - Utilizing advanced methods including differences-in-differences, propensity score matching, synthetic controls, and experimental design - 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 program evaluation, elasticity estimation, customer behavior analysis, and predictive modeling that accounts for seasonality and time trends - Build and refine comprehensive datasets for in-depth economic analysis - Present complex analytical findings to business leaders and stakeholders

Love using AI to solve big, messy problems that actually move a business? Come lead the science team that figures out where every advertiser is on their growth journey, and what to do next to help them achieve their goals. Advertising is one of the fastest growing parts of our business, and science sits at the heart of how we help brands succeed. We're looking for an experienced science leader to build, grow, and guide a team of applied scientists working on one of our most exciting challenges: figuring out, for each brand, the smartest next step they can take to grow, and turning that into a clear, personalized recommendation the people who work with those brands can act on right away. If you want real ownership, a team to grow, and problems that matter to millions of businesses, we'd love to talk. Key job responsibilities You'll lead and grow a team of applied scientists and software engineers working on an AI-first mission to understand where every advertiser is in their growth journey and turning that into clear, personalized guidance the people who work with those brands can act on. A big part of our work is understanding where each advertiser is in their growth journey using that to decide the right level of support each one requires to meet their goals. Day to day that means: Set the long-term scientific vision for modeling each advertiser's growth journey, pinpointing their best next opportunities, and shaping the multi-year roadmap to get there Guide the science that pulls many signals, a brand's health, retail, and advertising performance, into one unified, personalized set of recommendations for teams to act on. Hire, coach, and develop scientists and engineers, and build a team known for a high scientific bar. Partner with product and engineering to take models from early research into production, and make sure the guidance genuinely helps brands grow. Set quality standards, catch at-risk work early, and make the hard trade-off calls. A day in the life No two days look the same. You might start by reviewing a new modeling approach with your scientists, then meet with product and engineering partners to agree on what to build next. You'll dig into results, ask hard questions about whether a model is really moving the business, and coach a manager through a tricky call. Your customers are the teams who work directly with brands, and the brands themselves, who lean on your team's recommendations to decide where to invest and how to grow. About the team Sales AI is a central science and engineering organization within Amazon Advertising Sales. Our mission is to power selling motions and account team workflows with state-of-the-art AI and ML services. We're investing in a range of sales intelligence models, including advertiser insights, recommendations, and generative AI-powered applications woven throughout account team workflows. We care about a high scientific bar, real business impact, and taking ideas all the way into production. Most of all, we like working with curious people who want to drive real, lasting impact for our customers.

We are seeking a Research Scientist II to join our Electric Propulsion Subsystem team. In this role, you will be a significant and autonomous contributor, solving difficult engineering problems related to battery management systems (BMS) and energy storage. You will design, develop, and validate BMS hardware and algorithms from concept through production, working on high-voltage lithium-ion battery packs that push the boundaries of performance, safety, and energy density. This position requires an engineer who demonstrates strong technical depth in battery systems and BMS design while also exhibiting the system-level thinking and influence characteristic of our most impactful contributors. Key job responsibilities Design, develop, and validate BMS architectures for high-voltage lithium-ion battery packs from concept through volume production, including cell monitoring and sensing strategy, communication topology, and isolation design Develop safety controls, fault detection and diagnostic mechanisms, cell balancing routines, and thermal protection boundaries, and lead test plans validating BMS designs against safety standards (e.g., UL 2271, IEC 62619, UN 38.3) for functional safety, thermal resilience, and abuse tolerance Develop software algorithms for State of Charge (SOC), State of Health (SOH), and State of Power (SOP) estimation using machine learning models Support Hardware-in-the-Loop (HIL) testing, bench verification, and real-world data analysis to validate BMS algorithms and correlate models with physical pack performance Design charging infrastructure interfaces, including charge profile management, communication protocols, and interoperability with external charging systems Apply system-level thinking to understand how the BMS integrates with and influences the broader product architecture, including power electronics, thermal management, and vehicle control systems, and maintain high-quality design documentation (schematics, algorithm specs, analysis reports, test results)