Careers

We are looking for passionate, talented, and inventive Applied Scientists with a strong machine learning background to help build intelligent, AI-driven solutions that transform how Amazon manages travel and events at scale. As part of the Amazon Travel & Events (AT&E) Program Technology Solutions team, our mission is to provide a seamless and delightful experience for Amazon's business travellers and events programs by raising the bar in Generative AI with Large Language Models (LLMs), Natural Language Understanding (NLU), conversational AI, and Applied Machine Learning (ML). You will work alongside experienced engineers to develop and apply algorithms and modelling techniques that advance the state-of-the-art in conversational AI, intelligent automation, and data-driven decision making. You will gain hands-on experience with Amazon's heterogeneous travel data sources, including contracts, booking systems, supplier data, and event logistics—and large-scale computing resources to accelerate advances in travel and events intelligence at scale. You will also help make it easier for internal customers to use analytics to monitor and model program performance improvements. Key job responsibilities • Design, develop, and evaluate ML models leveraging GenAI, multimodal reasoning, and large-scale information retrieval to solve well-defined catalog understanding challenges such as product identity and relationship inference • Apply and adapt VLMs, foundation models, and LLM-based approaches to address product catalog problems—experimenting with fine-tuning, prompt engineering, and retrieval-augmented generation techniques • Implement model optimization techniques—including distillation, quantization, and serving optimizations—to improve latency, cost, and efficiency of deployed models under guidance from senior scientists • Drive the design and execution of rigorous experiments and ablation studies on large-scale datasets, delivering results with statistical rigor and clear recommendations to the team • Build and iterate on ML pipelines from prototyping through production deployment, writing clean, well-tested, production-quality code • Contribute to improving model reliability by applying uncertainty calibration, confidence estimation, and interpretability techniques to support trustworthy catalog decisions • Collaborate closely with senior scientists, engineers, and product teams to translate business requirements into well-scoped ML solutions • Stay current with the latest research in GenAI, VLMs, and multimodal AI, and identify opportunities to apply new techniques to team problems • Co-author research publications and contribute to internal tech talks and knowledge-sharing initiatives

The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As an Applied Scientist on the team, you will lead measurement solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. Key job responsibilities - Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement problems - Disambiguate problems to propose clear evaluation frameworks and success criteria - Work autonomously and write high quality technical documents - Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production - Partner closely with other scientists to deliver large, multi-faceted technical projects - Share and publish works with the broader scientific community through meetings and conferences - Communicate clearly to both technical and non-technical audiences - Contribute new ideas that shape the direction of the team's work - Mentor more junior scientists and participate in the hiring process About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.

Are you interested in changing how Amazon does marketing — moving beyond platform-optimized broad reach to campaigns that find the right customer, at the right moment, using Amazon's unmatched 1P data? We are seeking an Applied Scientist to join PRIMAS (Prime & Marketing Analytics and Science). In this role, you will design and run the experiments that answer the foundational question for EU marketing: does adding 1P audience signal on top of Value-Based Optimization (VBO) improve marketing efficiency — and if so, for which customer cohorts, on which surfaces, and at what scale? Amazon's current marketing model is largely platform-led: we set objectives and let platforms optimize toward conversion. This approach works well for broad acquisition but systematically underserves lifecycle goals — it cannot distinguish between a Bargain Hunter who will never pay full price and a high-potential customer one nudge away from becoming a Prime member. This role sits at the center of changing that. You will build the 1P audiences, design the experiments that test them, and generate the evidence that guides how Amazon allocates hundreds of millions in marketing spend. Year 1 is an experimentation year. You will deploy 1P audiences across multiple surfaces and channels — Meta, Google, Amazon Display Ads — and measure incrementally against VBO baselines. The goal is not to replace platform optimization but to understand when and where the combination of 1P signal + VBO outperforms VBO alone, and to build the experimental infrastructure that makes this learning scalable. Key job responsibilities 1P Audience Development & Experimentation: - Build and validate 1P audience segments from Amazon behavioral, transactional, and lifecycle data - Design experiments that isolate the incremental effect of 1P audience signal over platform VBO baselines - Deploy audiences across activation surfaces and establish measurement standards that make cross-surface comparison valid Causal Measurement & Incrementality: - Apply causal inference methods to measure the true incremental lift of audience-based targeting vs. VBO - Develop power analysis frameworks and guardrails that enable rapid experimentation without underpowered or conflated tests - Deliver optimization recommendations grounded in experimental evidence: which cohorts respond, which surfaces deliver, which creative strategies drive behavior change Scaling the Learning: - Build reusable audience and measurement frameworks that can be deployed across campaigns and channels — year 1 experiments should produce infrastructure, not one-off analyses - Document experimental learnings in a way that informs both the 2026 roadmap and the business case for investing further in 1P audience capabilities in 2027+ - Partner with engineering and PMT to translate validated audience prototypes into production-ready solutions that scale beyond the experimentation phase About the team The PRIMAS team, is part of a larger tech tech team of 100+ people called WIMSI (WW Integrated Marketing Systems and Intelligence). WIMSI core mission is to accelerate marketing technology capabilities that enable de-averaged customer experiences across the marketing funnel: awareness, consideration, and conversion.

Are you passionate about solving complex business problems at scale through Generative AI? Do you want to build intelligent systems that reason, act, and learn from minimal supervision? Are you excited about taking innovative AI solutions from proof-of-concept to production? If so, we have an exciting opportunity for you on Amazon's Trustworthy Shopping Experience (TSE) team. At TSE, our vision is to guarantee customers a worry-free shopping experience by earning their trust that the products they buy are safe, authentic, and compliant with regulations and policy. We give customers confidence that Amazon stands behind every product and will make it right in the rare chance anything goes wrong. We do this in close partnership with our selling partners and empower them with best-in-class tools and expertise required to offer a high-quality selection of compliant products that customers trust. As an Applied Scientist, you will lead the development of next Gen AI solutions to automate complex manual investigation processes at Amazon scale. You will work on some of the most fascinating challenges in applied AI—building systems that reason and act autonomously, learn rich representations from structured and relational data without extensive labels, adapt rapidly from limited examples, improve through feedback and interaction, seamlessly connect visual and textual understanding, and compress complex model capabilities into efficient, deployable systems. Your innovations will deliver significant impact to cost-of-serving customers while maintaining the highest standards of trust and safety. This role offers end-to-end ownership—from initial research and proof-of-concept through production deployment. You will see your innovations serving hundreds of millions of customers within months, not years. Key job responsibilities • Design and build expertise deep agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence with capabilities to handle feedback with long term as well as short term memory mechanisms. • Design and build expertise agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence with capabilities to handle feedback with long term as well as short term memory mechanisms. • Productionize large scale models built on top of SFT (Supervised Finetuning) and RFT (Reinforced fine tuning) approaches (GRPO with RLVR, Process/Outcome Reward Models), few shot approaches (Contrastive, Prototypical) based on multimodal datasets • Enhance on existing Automatic prompt optimization techniques (GEPA & beyond) towards agentic optimization given the ground truth datasets to improve agentic planning. • Build novel production ready Finetuned transformer architectures (using LORA/Q-LORA/LLM-JEPA etc) and conventional supervised & unsupervised ML solutions to aid the multiple potential automation requirements • Identify customer and business problems at project level; invent or extend state-of-the-art approaches for complex LLM workflows involving unstructured text, documents, images, and relational data • Author or co-author research papers for peer-reviewed venues; serve as PC member at conferences when aligned with business needs • Prototype rapidly, iterate based on feedback, and deliver components at SDE 1+ level that integrate directly into production-scale systems • Engineer efficient systems balancing model capability, deployment cost, and resource usage; write significant code demonstrating technical excellence and maintainability • Scrutinize algorithm and software performance for improvements; resolve root causes leaving systems more maintainable • Contribute to tactical and strategic planning—team goals, priorities, and roadmaps—while providing architectural guidance for AI systems • Participate in engineering best practices with rigorous peer reviews; communicate design decisions clearly and participate in science reviews • Train new teammates & interns on component construction and integration; mentor less experienced scientists and participate in hiring processes About the team Investigation technology Product team in TSE is responsible for the human-in-the-loop products and technology used in the risk investigations at Amazon. The team is also responsible for reducing the cost of performing the investigations, by automating wherever possible and optimizing the experience where manual interventions are needed. The team leverages state-of-the art technology and GenAI to deliver the products and associated goals.

We are seeking an experienced Data Scientist to drive scientific tooling supporting how Amazon's business customers interact with LTPF forecasts and plans. As a science leader within the LTPF, you will be responsible for building to the multi-year roadmap for customer engagement, ensuring that business stakeholders across Amazon can seamlessly access, understand, and act upon our forecasting outputs. In this role, you will manage the lifecycle of complex, cross-functional programs that transform how Operations, Stores, and Finance teams leverage LTPF insights for strategic decision-making. You will work with scientists, economists, engineers, and business customers to architect the customer interaction experience, including viewing capabilities, auditing tools, what-if analysis frameworks, and forecast intervention workflows. This role might be for you if you have interest and experience in: - Leading large, cross-functional planning and strategy workstreams that impact Amazon's topline growth - Defining multi-year program vision and strategy while balancing short-term execution - Regularly presenting to VP and SVP level leaders - Prioritizing operational excellence work alongside feature delivery on a roadmap - Showing strong business acumen with strategic, analytical, and critical thinking - Managing planning calendars and strategic review mechanisms - Driving organizational alignment across multiple teams and stakeholders Key job responsibilities As a Data Scientist in LTPF (Long-Term Planning & Forecasting): - You will develop causal inference models, automated explainability frameworks, and variance bridging methodologies that translate LTPF's forecasts and plans into actionable business intelligence. - Your work will enable leadership to understand why forecasts and actuals diverge, what is driving demand shifts, and how strategic decisions propagate through the planning ecosystem. - You will build automated Plan-vs-Actual and Actual-vs-Actual variance decomposition models that quantify the contribution of individual demand drivers to observed gaps across revenue, price, units, inventory, and capacity metrics at multiple granularities to serve audiences from working-level analysts to VP-level planning reviews cycles. - You will build and maintain a causal model library with standardized hypothesis generation and validation pipelines, applying techniques from causal inference, time-series econometrics, and Bayesian methods. Each model will include calibrated confidence scoring and reusable components that scale across worldwide marketplaces. - You will develop GenAI-powered narrative generation capabilities that synthesize quantitative variance outputs into human-readable performance summaries and design automated hypothesis ranking to determine which demand drivers are most responsible for observed forecast error. A day in the life 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 The Long-Term Planning and Forecasting (LTPF) organization is dedicated to answering some of Amazon's most important strategic questions: Where will Amazon's growth come from in the next year? What about over the next five years? Which product lines are poised to grow significantly? Are we investing appropriately in our infrastructure? How do our customers react to changes in prices, product selection, or delivery times? Are our infrastructure investments optimal for the level of demand we expect?

We are seeking an experienced Data Scientist to drive scientific tooling supporting how Amazon's business customers interact with LTPF forecasts and plans. As a science leader within the LTPF, you will be responsible for building to the multi-year roadmap for customer engagement, ensuring that business stakeholders across Amazon can seamlessly access, understand, and act upon our forecasting outputs. In this role, you will manage the lifecycle of complex, cross-functional programs that transform how Operations, Stores, and Finance teams leverage LTPF insights for strategic decision-making. You will work with scientists, economists, engineers, and business customers to architect the customer interaction experience, including viewing capabilities, auditing tools, what-if analysis frameworks, and forecast intervention workflows. This role might be for you if you have interest and experience in: - Leading large, cross-functional planning and strategy workstreams that impact Amazon's topline growth - Defining multi-year program vision and strategy while balancing short-term execution - Regularly presenting to VP and SVP level leaders - Prioritizing operational excellence work alongside feature delivery on a roadmap - Showing strong business acumen with strategic, analytical, and critical thinking - Managing planning calendars and strategic review mechanisms - Driving organizational alignment across multiple teams and stakeholders Key job responsibilities As a Data Scientist in LTPF (Long-Term Planning & Forecasting): - You will develop causal inference models, automated explainability frameworks, and variance bridging methodologies that translate LTPF's forecasts and plans into actionable business intelligence. - Your work will enable leadership to understand why forecasts and actuals diverge, what is driving demand shifts, and how strategic decisions propagate through the planning ecosystem. - You will build automated Plan-vs-Actual and Actual-vs-Actual variance decomposition models that quantify the contribution of individual demand drivers to observed gaps across revenue, price, units, inventory, and capacity metrics at multiple granularities to serve audiences from working-level analysts to VP-level planning reviews cycles. - You will build and maintain a causal model library with standardized hypothesis generation and validation pipelines, applying techniques from causal inference, time-series econometrics, and Bayesian methods. Each model will include calibrated confidence scoring and reusable components that scale across worldwide marketplaces. - You will develop GenAI-powered narrative generation capabilities that synthesize quantitative variance outputs into human-readable performance summaries and design automated hypothesis ranking to determine which demand drivers are most responsible for observed forecast error. A day in the life 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 The Long-Term Planning and Forecasting (LTPF) organization is dedicated to answering some of Amazon's most important strategic questions: Where will Amazon's growth come from in the next year? What about over the next five years? Which product lines are poised to grow significantly? Are we investing appropriately in our infrastructure? How do our customers react to changes in prices, product selection, or delivery times? Are our infrastructure investments optimal for the level of demand we expect?

Estimating the long-run customer value of a pricing decision is genuinely hard. The causal effects are delayed, noisy, and confounded by factors that standard experiment analysis wasn't designed to handle. Most pricing teams default to short-run metrics not because they don't care about long-run outcomes, but because measuring them rigorously is an unsolved problem. P2OS is building the science to solve it. We're hiring a Sr. Economist to own that work — defining how we estimate customer lifetime value in a pricing context, building the identification strategies that make those estimates credible, and translating outputs into something pricing teams can use to make better decisions. The role sits at the intersection of econometric methodology and production-quality analysis, and requires someone who can operate independently in both. As science lead, you'll own the LTV methodology domain, develop the economists and scientists on your scrum, and be the internal authority on causal inference for pricing across P2OS and partner teams. Key job responsibilities * Own the end-to-end LTV methodology for pricing — identification strategy, modeling choices, validation approach, and business use cases — and drive adoption across pricing contexts * Deliver high-stakes analyses connecting LTV estimates to a concrete pricing decision and strategy change at VP+ level * Apply advanced causal methods to live pricing problems; document approaches so the team can build on and extend them. * Provide causal inference guidance on pricing experiment questions as they arise — being the methodology resource when experiments generate LTV-relevant questions * Serve as cross-team economic advisor to Finance, Customer Behavior, and Demand Science on LTV assumptions and causal identification * Actively mentor junior scientists, earn trust of cross-functional tech and product partners. A day in the life In a typical day, you'll move between methodology work and stakeholder-facing analysis. - On the science side, that means reviewing identification assumptions with the Causal AS, validating estimation choices for the LTV framework, and documenting methodology decisions in ways that non-economists can act on. - On the applied side, you'll be in rooms with Finance, Pricing PMs, and other science teams: aligning on LTV definitions, resolving disagreements between competing metrics, and translating causal findings into recommendations that land in strategy reviews. - As tech lead, you need to work to develop the economists and scientists on your scrum: structured reviews, identification strategy feedback, and raising the quality of analyses before they reach stakeholders. The mix shifts, but the through-line is to progress the LTV methodology from open questions to shipped frameworks, and making sure the team's causal work is rigorous enough to hold up when it counts. About the team P2Optimization Science (P2OS) is responsible for the ML models and analytical frameworks that drive pricing decisions at scale. The team spans demand lift modeling, pricing error detection, customer lifetime value, and experimentation. Our small team of specialized applied scientists and economists works closely alongside engineers, and pricing product managers.

We are seeking an Applied Science Manager to lead a team building Amazon’s next-generation customer memory and personalization systems. Are you interested in building systems that move beyond reacting to customer behavior, to actually understanding and remembering it over time? Our team is building Amazon’s customer memory layer – a system that extracts, curates, and reasons over customer knowledge to power next-generation personalization. This includes transforming noisy, unstructured signals into durable, high-quality representations of customer preferences, intents, and life events, and using them in real time to improve customer experiences. We are part of Amazon’s Personalization organization, a high-performing group that leverages large-scale machine learning, generative AI, and distributed systems to deliver highly relevant customer experiences. We tackle challenging problems at the intersection of information extraction, knowledge representation, LLM reasoning, and recommendation systems. Our systems operate under real-world constraints of scale, latency, and quality, requiring careful tradeoffs between precision, recall, and responsiveness. This team plays a central role in defining how Amazon understands its customers, and how that understanding is applied across the shopping experience. As an Applied Science Manager, you will lead a team of scientists working on LLM-powered memory and personalization systems. You will define the scientific direction for how customer knowledge is extracted, validated, and applied in production systems. Key job responsibilities As an Applied Science Manager, you will lead a team of scientists working on LLM-powered memory and personalization systems. You will define the scientific direction for how customer knowledge is extracted, validated, and applied in production systems. You will own the end-to-end delivery of ML solutions, from problem formulation and modeling to offline and online experimentation, and production deployment at scale. You will ensure your team delivers high-quality, scalable systems that power customer-facing experiences. You will drive work across areas such as fact extraction, memory quality and lifecycle, temporal reasoning, and grounded personalization, while navigating tradeoffs between quality, latency, and coverage. You will hire and develop scientists, raise the scientific bar, and foster a culture of ownership and rigor. You will partner closely with engineering and product teams to translate research into measurable customer impact, and represent your team’s work to senior leadership. Please visit https://www.amazon.science for more information.

Interested in influencing what customers around the world see when they turn on Prime Video? The Prime Video Personalization and Discovery team matches customers with the right content at the right time, at all touch points throughout the content discovery journey. We are looking for a customer-focused, solutions-oriented Sr Manager Data Science and Analytics to develop next-gen measurement and experimentation systems within Prime Video Personalization and Discovery. You'll lead an embedded science and business intelligence team driving projects across product and engineering teams that ultimately influence what millions of customers around the world see when the log into Prime Video. The ideal candidate brings experience leading experiment-based measurement and business intelligence engineering systems, excellent stakeholder communication skills, and the ability to balance technical rigor with delivery speed and customer impact. You will build cross-functional support within Prime Video for high-quality, rigorous measurement, assess business problems, and support iterative scientific solutions that balance short-term delivery with long-term science roadmaps. Key job responsibilities Key job responsibilities - Build and manage a team of Data Scientists, BI Engineers and Data Engineers - Define and drive the multi-year vision for business intelligence, experimentation and measurability in Prime Video - Partner with product stakeholders and science peers to identify strategic data-driven opportunities to improve the customer experience - Communicate findings, conclusions, and recommendations to technical and non-technical business leaders across Prime Video - Educate senior leaders about and advocate for high-quality measurement as an input to data-driven decisions - Mentor junior scientists, BI Engineers and review technical artifacts to ensure quality - Stay up-to-date on the latest data science, measurability, AI tools, techniques, and best practices and help evangelize them across the organization

Amazon’s Last Mile Team is looking for a passionate individual with strong optimization and analytical skills to join its Last Mile Science team in the endeavor of designing and improving the most complex planning of delivery network in the world. Last Mile builds global solutions that enable Amazon to attract an elastic supply of drivers, companies, and assets needed to deliver Amazon's and other shippers' volumes at the lowest cost and with the best customer delivery experience. Last Mile Science team owns the core decision models in the space of jurisdiction planning, delivery channel and modes network design, capacity planning for on the road and at delivery stations, routing inputs estimation and optimization. Our research has direct impact on customer experience, driver and station associate experience, Delivery Service Partner (DSP)’s success and the sustainable growth of Amazon. Optimizing the last mile delivery requires deep understanding of transportation, supply chain management, pricing strategies and forecasting. Only through innovative and strategic thinking, we will make the right capital investments in technology, assets and infrastructures that allows for long-term success. Our team members have an opportunity to be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, scientists, and software engineers in the industry. Key job responsibilities Candidates will be responsible for developing solutions to better manage and optimize delivery capacity in the last mile network. The successful candidate should have solid research experience in one or more technical areas of Operations Research or Machine Learning. These positions will focus on identifying and analyzing opportunities to improve existing algorithms and also on optimizing the system policies across the management of external delivery service providers and internal planning strategies. They require superior logical thinkers who are able to quickly approach large ambiguous problems, turn high-level business requirements into mathematical models, identify the right solution approach, and contribute to the software development for production systems. To support their proposals, candidates should be able to independently mine and analyze data, and be able to use any necessary programming and statistical analysis software to do so. Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs.