Careers

The AWS Compliance & Security Assurance Engineering team builds tools and services that scale AWS's ability to exceed security and compliance expectations for our regulators, auditors, and customers globally. We create efficiencies for our teams while maintaining transparency for our customers. As a Data Scientist on our data team, you'll have a unique opportunity to build solutions that scale security assurance capabilities through AI-driven approaches. You'll leverage advanced analytics, machine learning techniques, and state-of-the-art AI technologies on top of highly complex datasets to transform our security assurance operations. You'll also help shape our data science roadmap, fostering data-driven decision-making and delivering significant business impact through innovative methodologies. The ideal candidate brings a strong background in automation by leveraging GenAI Large Language Models (LLMs), combined with experience analyzing complex datasets and a proven ability to transform business needs into deliverables. If you're passionate about applying AI to security challenges in a dynamic environment that offers startup-like autonomy with enterprise impact, we want to hear from you. Key job responsibilities * Analyze and extract hidden insights from complex, large-scale datasets using advanced statistical and AI techniques, using hands-on expertise in data wrangling and manipulation. * Identify high-impact business improvement opportunities, lead the design and execution of data science initiatives to address them. * Apply statistical and ML knowledge to specific business problems and data. * Leverage Vision Language Models (VLMs) and Large Language Models (LLMs) to analyze document/image structure, perform content understanding and metadata extraction. * Rapidly prototype and test AI solutions, while iterating quickly based on data and feedback. * Collaborate cross-functionally to deeply understand business requirements, customer needs, and technical constraints; transforming discovered information into data-driven solutions and actionable recommendations. * Communicate complex technical concepts to technical and non-technical stakeholders; present compelling insights and evidence-based recommendations to leadership. * Stay up-to-date on the latest advancements in AI and identify opportunities to apply emerging techniques to the space. * Collaborate with Business Intelligence, Data Engineers and SDEs to drive the collection of new data and refinement of existing data sources to continually improve data quality. * Actively participate in team design reviews, data modeling discussions, brainstorming activities while mentoring colleagues in state-of-the-art AI tools and methodologies. About the team Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & 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, training, 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Come build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies — all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business — available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. Prime Video Commerce's mission is to present the right offer to the right customer at the right time — across subscriptions, channels, and transactional video in every market and on every device. Our science team replaces static business rules with ML-driven decisions that personalise the entire commerce journey, from discovery through to checkout and beyond. We operate at scale across hundreds of millions of customers, and we are now expanding into new frontiers — combining the latest advances in agentic and generative AI, behavioural simulation, and causal inference to understand the impact of our decisions before they reach customers. We are looking for an Applied Scientist to join the Prime Video Commerce Insights team who will work on the latest research and machine learning to build scalable personalisation solutions. You will develop and deploy customer-facing models, understand customer behaviour at scale, and explore emerging techniques that help us make better decisions faster. This is a hands-on role working with a high performing and high visibility multidisciplinary group of engineers and scientists in the London office, focused on improving the customer experience for Prime Video and the wider Amazon organization. You will contribute to the design of machine learning models that scale to large quantities of data and serve low-latency recommendations to all customers worldwide. You will embody scientific rigor in designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science and engineering team that embodies the customer obsession principle by developing recommendation and decision systems that raise the profile of Prime Video Commerce as a global leader in machine learning and personalisation. Successful candidates will have strong technical ability, a focus on customers by applying a customer-first approach, and excellent teamwork and communication skills. The position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. Key job responsibilities - Research, design, and implement recommendation systems that personalise across different customer experience touch points. - Collaborate with engineers to deploy and integrate successful model experiment results into large-scale, complex Amazon production systems with low latency. - Provide machine learning thought leadership to both technical and business leaders, with the ability to think strategically about business, product, and technical challenges. - Be a subject matter expert in reinforcement learning approaches for the team and actively contribute to the science roadmap - Define the science roadmap and research agenda that aligns with the organisation's priorities and production constraints. - Work with technical product managers to work backwards from what's important to customers and deliver machine-backed solutions. - Report and share results with the team and wider scientific community by authoring documents that are both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment. A day in the life You will be both a research leader and a hands-on innovator within the Commerce Insights organisation. You'll collaborate with talented engineers and senior leaders to solve problems that are uniquely challenging at Amazon's scale: personalising commerce decisions across multiple business lines balancing competing objectives across offerings, and positively impacting hundreds of millions of customers worldwide. The problems here are technically deep — combining large-scale ML, causal reasoning, and behavioural modelling in a domain where every decision carries real revenue and customer experience consequences. Your research will ship to production and move metrics that matter. About the team You will join a team of great team of engineers and applied scientists with a proven track record of solving highly complex, ambiguous problems — work that has produced patents and publications at top-tier conferences. The team has direct visibility to senior Prime Video leadership, and collaborates broadly across Commerce, Content, and Platform teams to shape how customers discover, subscribe to, and engage with video content. This is a team that operates at the intersection of rigorous research and real-world impact, where your ideas move from whiteboard to production for hundreds of millions of customers.

Amazon's Customer Experience and Business Trends (CXBT) is seeking a Data Science Manager to lead a team of scientists and engineers within Benchmarking Economics Analytics and Measurement (BEAM). BEAM is a central analytics and science function that drives Amazon's quantification of CX improvement opportunities through comparative benchmarks, partnering with stakeholders across CXBT, business domain teams, Finance, SCOT, and other centralized science teams. This is a hands-on leadership role for a manager who can set technical direction, build durable data products, and grow people. You will own the strategy and roadmap for a portfolio of analytics products, working backward from leadership and stakeholder needs to deliver insights that inform decisions at the speed of business. Key job responsibilities - Build a holistic metrics and trend-detection product. Lead the team to design and operationalize an always-on framework of indicators that surfaces emerging business trends reliably enough to brief senior leaders. - Partner with cross-org stakeholders to drive product adoption and impact. Work directly with internal customers and partner teams to ensure our products are tightly aligned with business use cases, translate ambiguous problems into well-scoped analytics solutions, and drive adoption so that insights translate into decisions and measurable business impact. - Manage, mentor, and grow the team. Hire, develop, and retain a high-performing team of scientists and engineers. Set clear expectations, give actionable feedback, create stretch opportunities, and build the bench strength needed to scale the team's scope over time. - Lead the transformation from traditional analytics to a GenAI-native operating model. Shape and execute the team's technical strategy to evolve from manual, study-based analytics toward GenAI-enabled products and workflows — accelerating insight generation, improving self-serve access for stakeholders, and freeing capacity for deeper scientific investment.

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! Amazon continues to develop its advertising program. Ads run in our Stores (including Consumer Stores, Books, Amazon Business, Whole Foods Market, and Fresh) and Media and Entertainment publishers (including Fire TV, Fire Tablets, Kindle, Alexa, Twitch, Prime Video, Freevee, Amazon Music, MiniTV, Audible, IMDb, and others). In addition to these first-party (1P) publishers, we also deliver ads on third-party (3P) publishers. We have a number of ad products, including Sponsored Products and Sponsored Brands, display and video products for smaller brands, including Sponsored Display and Sponsored TV. We also operate ad tech products, including Amazon Marketing Cloud (a clean-room for advertisers), Amazon Publisher Cloud (a clean-room for publishers), and Amazon DSP (an enterprise-level buying tool that brings together our ad tech for buying video, audio, and display ads). Key job responsibilities This role is focused on diving deep into Amazon Ads data, especially full funnel ads campaigns, a new AI-driven workflow provided to advertisers. Rolling out this workflow at scale is critical for Amazon in 2026.

Are you passionate about applying machine learning, time series forecasting, and operations research to transform the delivery of heavy and bulky items for Amazon customers? Are you excited about working with large-scale operational data and developing models that drive real business impact? If so, the Amazon Extra Large (AMXL) Science team may be the right fit for you. AMXL is Amazon's specialized business for delivering heavy and bulky items — appliances, furniture, fitness equipment, and mattresses — with a premium customer experience that includes room-of-choice delivery, at-home installations, and assembly services. In this role, you will leverage large-scale operational data to develop and deploy predictive models and optimization solutions that solve real-world logistics and fulfillment challenges, partnering closely with scientists, engineers, and business stakeholders. Key job responsibilities Apply machine learning, statistical modeling, time series analysis, and operations research techniques to build solutions for delivery routing, capacity planning, demand forecasting, workforce scheduling, and network optimization Analyze large-scale historical and real-time operational data to surface efficiency patterns, bottlenecks, and emerging trends across the AMXL network Develop, validate, and deploy models that improve cost-to-serve and customer experience Partner with cross-functional teams to implement data-driven strategies and measure impact Build scalable, automated pipelines for data ingestion, feature engineering, model training, and validation Monitor deployed model performance and communicate results through clear reporting on key operational and business metrics A day in the life You'll be part of a small, collaborative team of scientists who move fast and care deeply about the problems they solve. A typical week might involve whiteboarding a new forecasting approach with a senior scientist, partnering with engineers to push a model into production, deep-diving into operational data to understand why a metric moved, or presenting your findings to business leaders who will act on them. The work is high-visibility and high-impact. The models you build will directly influence how millions of heavy and bulky items reach customers. About the team The AMXL Science team is a worldwide group of data scientists, applied scientists, and product managers solving Amazon's most complex heavy bulky supply chain challenges. We build forecasting models, capacity planning systems, and optimization tools that directly impact millions of customer deliveries. Our culture values scientific rigor, measurable business impact, and clear communication. We start with baselines, earn complexity, and partner closely with operations to ensure our work drives real decisions. You'll tackle problems where logistics constraints demand creative, data-driven solutions — and see your models shape labor planning, routing, and customer experience at scale.

Are you passionate about solving complex business problems at scale through Generative AI? Do you want to help build intelligent systems that reason, act, and learn from minimal supervision? 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 do this in close partnership with our selling partners, empowering them with best-in-class tools and expertise to offer a high-quality, compliant selection that customers trust. As an Applied Scientist I, you will bring subject matter expertise in at least one relevant discipline (e.g., NLP, computer vision, representation learning, agentic architecture) to contribute to next-generation agentic AI solutions that automate complex manual investigation processes at Amazon scale. Working alongside senior scientists, you will map business goals—such as reducing cost-of-serving while maintaining trust and safety standards—to well-defined scientific problems and metrics. You will invent, refine, and experiment with solutions spanning agentic reasoning, self-supervised representation learning, few-shot adaptation, multimodal understanding, and model compression. With guidance from senior scientists, you will stay current on research trends and benchmark your results against the state of the art. You will help design and execute experiments to identify optimal solutions, initiating the development and implementation of small components with team guidance. You will write secure, stable, testable, and well-documented production code at the level of an SDE I, rigorously evaluating models and quantifying performance. You will handle data in accordance with Amazon policies, troubleshoot issues to root cause, and ensure your work does not put the company at risk. Your scope of influence will typically be at the self-level, with the possibility of mentoring interns. You will participate in team design and prioritization discussions, learn the business context behind TSE's products, and escalate problems with proposed solutions. You will publish internal technical reports and may contribute to peer-reviewed publications and external review activities when aligned with business needs. This role offers a unique opportunity to contribute to end-to-end AI development—from research through production—with your contributions serving hundreds of millions of customers within months, not years. Key job responsibilities •Contribute to the design and development of agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence, including feedback and memory mechanisms, leveraging reinforcement learning techniques for agent decision-making and policy optimization, with input and guidance from senior scientists • 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 •Contribute to building production-ready deep learning and conventional ML solutions, including multimodal fusion and cross-modal alignment techniques that seamlessly connect visual, textual, and relational understanding, to support automation requirements within your team's scope •May co-author research papers for peer-reviewed internal and/or external venues, including contributions in areas such as multimodal representation learning and vision-language modeling, and contribute to the wider scientific community by reviewing research submissions, when aligned with business needs •Prototype rapidly, iterate based on feedback, and deliver small components at SDE I level—including multimodal data pipelines and inference modules—that integrate into production-scale systems •Write secure, stable, testable, maintainable, and well-documented code, balancing model capability, deployment cost, and resource usage across multimodal architectures while understanding state-of-the-art data structures, algorithms, and performance tradeoffs •Rigorously test code and evaluate models across individual and combined modalities, quantifying their performance; troubleshoot issues, research root causes, and thoroughly resolve defects, leaving systems more maintainable •Participate in team design, scoping, and prioritization discussions through clear verbal and written communication; seek to learn the business context, science, and engineering behind your team's products, including how multimodal signals contribute to trust and safety decisions •Participate in engineering best practices with peer reviews; clearly document approaches and communicate design decisions; publish internal technical reports to institutionalize scientific learning •Help train and mentor scientist interns; identify and escalate problems with proposed solutions, taking ownership or ensuring clear hand-off to the right owner About the team Trustworthy Shopping Experience 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.

This role will develop Economic and Econometric models and thought leadership on projects that span our business, with a particular focus on supply chain and inventory selection. Through the lens of economics, you will develop a deeper understanding of how customers value our selection and interact with Amazon's store to make shopping easier. You will be a key and senior scientist, advising Amazon leaders how to evaluate selection and understand customers. You will work on developing frameworks and scaleable, repeatable models supporting optimal pricing and policy in the two-sided marketplace that is central to Amazon's business. The pricing for Amazon services is complex. You will partner with science and technology teams across Amazon including Supply Chain, Operations, Retail, FBA, Consumer Pricing, and Finance. Key job responsibilities The ideal candidate will have extensive Economics knowledge, demonstrated strength in practical and policy relevant structural econometrics, strong collaboration skills, proven ability to lead highly ambiguous and large projects, and a drive to deliver results. Ability to incorporate AI tools and methods into your work is highly valued. They will work closely with Economists, Data/Applied Scientists, Software Engineers and Product Managers to integrate economic insights into policy and systems production. About the team The Stores Economics and Sciences team is a central science team that supports Amazon's Retail and Supply Chain leadership. We tackle some of Amazon's most challenging economics and machine learning problems, where our mandate is to impact the business on massive scale.

Amazon's Customer Service (CS) department is seeking a senior Data Scientist to lead the scientific direction of the Data Analytics Support Hub (DASH) Advanced Analytics team. CS is the heart of Amazon; our vision is to be Earth's most customer-centric company. The successful candidate will be the scientific leader within the Advanced Analytics branch, setting the methodological bar and driving Q&E's most complex diagnostic and predictive analytics across a worldwide, cross-vertical scope. As a Data Scientist III, you will define the scientific strategy for Q&E's transition from descriptive to diagnostic and predictive analytics. You will own the measurement frameworks for pioneering KPIs where no prior art exists, lead the multi-contact journey science (Transfers, Repeats, DART, ECR/VPI), and be the scientific voice in partnership with central teams. You will be hands-on on 2-3 flagship programs while being accountable for the scientific bar across the entire branch. Key job responsibilities Responsibilities include but are not limited to: - Set the scientific direction for the Advanced Analytics branch across flagship initiatives. - Define measurement frameworks for Q&E-pioneering KPIs where no prior art exists (QoS, FIR, Outlier Behavior). - Own the scientific framework for multi-contact journey analysis: threading interactions, attributing root cause across touchpoints, separating preventable vs. necessary events. - Choose the right methods (statistical, causal, ML, LLM, hybrid) for each problem and justify trade-offs. Drive excellence in evaluation: ground-truth construction with Quality auditors, human audits, precision/recall, drift, calibration, bias, safety, and cost. - Design driver-analysis and bridging methods that explain KPI movement (WoW, MoM, YoY, vs OP2) across dimensions for WBR "why" automation consumed by senior leadership. - Represent DASH in Senior Manager / Director reviews, CS-LT forums, and partner-team design reviews. Build consensus on contentious scientific and architectural decisions. - Partner with Data Engineers on productionization, Shepherd risk, App Security red-certification, Kale, Legal, Threat Models, for scientific assets. - Mentor team members; provide promotion assessments; contribute to hiring at DS II and DS III. Represent Q&E in the broader Amazon Data Science community. - Produce design docs, technical documentation, and review artifacts.

Are you a scientist interested in pushing the state of the art in machine learning and recommendation systems? Are you interested in working on novel ideas that can positively impact millions of customers? Do you wish you had access to large datasets and tremendous computational resources? Answer yes to any of these questions and you will be a great fit for our team at Amazon. Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, big data, distributed systems, and user experience design to deliver the best shopping experiences for our customers. Our team builds large-scale machine-learning solutions that delight customers with personzlized content recommendations, at the right time, with the right level of explanation. As an Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems. Please visit https://www.amazon.science for more information.

Are you a scientist interested in pushing the state of the art in machine learning and recommendation systems? Are you interested in working on novel ideas that can positively impact millions of customers? Do you wish you had access to large datasets and tremendous computational resources? Answer yes to any of these questions and you will be a great fit for our team at Amazon. Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, big data, distributed systems, and user experience design to deliver the best shopping experiences for our customers. Our team builds large-scale machine-learning solutions that delight customers with personzlized content recommendations, at the right time, with the right level of explanation. As an Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems. Please visit https://www.amazon.science for more information.