Careers

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 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 leader who can build and guide the team that pushes it forward. As an Applied Science Manager on this team, you will own both the scientific vision and the people strategy for our applied science function. You will lead a diverse team of scientists working at the intersection of causal inference and machine learning — setting the technical direction, raising the bar on modeling and engineering practices, and ensuring that research translates into production systems that leaders use to make better workforce decisions every day. You will work closely with economists who deeply understand the causal mechanisms driving workforce dynamics, data scientists who know the operational landscape, and a dedicated partner engineering team that productionizes your team's work. This is not a role where you manage from a distance. You will stay close to the science — reviewing model designs, shaping feature engineering strategies, and guiding your team through the ambiguity of novel problem spaces including large language models, computer vision, and other emerging techniques applied to workforce challenges. At the same time, you will build the team culture, operating mechanisms, and talent pipeline needed to scale our applied science capabilities as the organization grows. This role is built for someone who is both a strong technical scientist and a genuine people leader — someone who gets energy from developing others, who can translate between disciplines, and who sees building a high-performing team as one of the most impactful things they can do. You will partner with stakeholders and senior leadership to define priorities, communicate results, and drive the adoption of science-informed workforce strategy across Amazon's operations. If you want to lead a team doing science that directly shapes how Amazon supports its workforce — not in theory, but in production systems that drive real decisions at scale — we'd love to talk. Key job responsibilities • Manage and develop a high-performing team of scientists — fostering innovation and scientific rigor while providing coaching, mentorship, and clear growth paths • Establish operating mechanisms and performance expectations to track and communicate team progress • Own hiring and talent strategy for the applied science function, including hiring and conversion for other job families • Set and execute the scientific vision for the applied science function — bringing deep ML expertise to the team's causal predictive modeling agenda and identifying where advanced methods (deep learning, LLMs, computer vision, novel architectures) can strengthen the causal frameworks and unlock signal that traditional approaches miss • Establish standards for code quality, documentation, and scalability to ensure your team's work can be implemented directly into operational decision-making tools by partner engineering teams • Bridge economists, data scientists, research scientists, and engineers — synthesizing causal rigor with ML innovation to produce models that are scientifically defensible and operationally useful • Partner with stakeholders and senior leadership to define priorities, drive adoption of science-informed workforce strategy, and leverage the broader scientific community • Distill complex causal and predictive findings into clear recommendations for senior leadership that drive workforce strategy for Amazon's hourly populations • Define team structure, strategic direction, and owned technologies, adjusting priorities and removing roadblocks to optimize outcomes 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.

Every time a customer checks out, a split-second decision determines which payment method appears, in what ranking, whether there is payment failure risk and how frictionless the experience feels. That decision touches 300MM+ customers and 2B+ monthly transactions — and you'll be the scientist building the intelligence behind it. Are you excited by the challenge of applying machine learning, GenAI, and real-time personalization to one of the highest-volume, lowest-latency decision systems at Amazon? Do you want to build models that directly move billions in revenue — predicting payment risk before it happens, recommending the right payment method at the right moment, and eliminating friction that customers shouldn't have to think about? Join Payment Acceptance & Experience (PAE) Data Science, where you'll build the ML systems that power Amazon's Payment Experience Intelligence. You'll take models from conception to production alongside scientists, engineers, and product managers, shipping at a scale few teams in the industry can match. Key job responsibilities -Build and ship ML systems at scale — design, develop, evaluate, deploy, and monitor ML models that personalize payment experiences for 300MM+ customers across 2B+ monthly transactions. -Solve global problems once — develop worldwide models that scale across business lines and locales with minimal adaptation, and continuously improve model performance and ML architecture. -Own the full lifecycle — contribute production-grade code and science tooling, from experimentation framework to deployed inference. -Measure real impact — design A/B experiments, conduct rigorous statistical analysis, and translate results into product and business decisions. -Ship with engineering and product partners — collaborate with SDEs to take models from prototype to production, and with business stakeholders to drive alignment on science-informed strategy. -Advance the science — present and publish research internally and externally, contributing to Amazon's science community and raising the bar for the field.ommunity About the team Payment Acceptance and Experience's (PAE) mission is to **build the most trusted, intuitive, and accessible payment experience on earth**. The team provides new and existing customers, anywhere in the world, the ability to pay on- and off-Amazon, with world-class ease of use, payment method variety, and security. The PAE Data Science team builds AI-native intelligence systems that improve payment experience, optimize marketing efficiency, automate operational workflows, and enable faster business decision-making across the payments ecosystem. It serves as a high-leverage, strategic engine advancing PAE's mission.

We are seeking an Applied Science Manager to lead the Cost-to-Serve science team within JCI MOST (Measurement and Optimization Science Team). This team builds the causal models, optimization systems, and AI-driven analytics that power Amazon Japan's CtS program — identifying where cost saving opportunities exist across the supply chain, explaining why they exist, and quantifying their dollar impact. You will manage a team of applied scientists, economists, and data scientists working across causal inference, consolidation optimization, supply chain forecasting, and GenAI-powered analytics. Your team's work directly shapes how VP-level leadership makes investment decisions across CtS levers in Amazon Japan. Key Responsibilities -Lead and grow a team of scientists delivering causal models, optimization engines, and AI-driven insights for supply chain cost reduction -Set the science roadmap and prioritize across workstreams: causal attribution, financial simulation, forecasting, and GenAI agent development -Partner with product, engineering, operations, and finance to translate science into operational impact -Drive the integration of science models into AI tools — making causal reasoning accessible to non-technical stakeholders at scale -Represent CtS science to VP-level leadership through MBR/QBR mechanisms and OP planning At Amazon, you'll work alongside the latest AI and GenAI tools that are increasingly woven into how teams operate: from AI-powered capabilities that accelerate decision-making, to Generative AI that helps you focus on work that truly matters. You'll have opportunities and resources to develop AI fluency at your own pace, with continuous learning built into the culture.

The OTS ANCHOR (Analytics, Insights, and Centralized Hub for OTS Reporting) team is part of the WW IT Operations & RME organization, responsible for delivering data solutions that drive operational excellence across Amazon's global IT infrastructure. We build data pipelines, predictive models, develop analytical intelligence frameworks, and create AI-powered solutions that transform how Amazon manages, triages, and resolves IT operational incidents at scale pointing towards the Letting Data Tell the Story north start vision. We are seeking a Senior Data Scientist to own the end-to-end data science charter for critical operational workstreams — from forecasting IT service demand and capacity planning to designing intelligent severity triaging models and building the analytical backbone for agentic AI solutions. You will work backwards from complex operational problems to create models and solutions that directly impact the efficiency and reliability of Amazon's IT operations worldwide. Key job responsibilities - Design, develop, and deploy machine learning models for IT operations forecasting, including incident volume prediction, capacity planning, and workforce demand modeling - Build and maintain severity classification and intelligent triaging models that process thousands of daily IT service tickets across global regions - Develop anomaly detection systems to identify emerging patterns in operational data, enabling proactive incident prevention - Create econometric and statistical frameworks to measure the impact of operational interventions and optimize resource allocation decisions - Partner with engineering teams to productionize ML models via automated pipelines using AWS services (SageMaker, Step Functions, Lambda, Redshift) - Design and conduct experiments to empirically validate model performance and operational impact - Develop data quality monitoring frameworks and establish standards for operational data integrity - Collaborate with cross-functional teams (ServiceNow platform, Decision Intelligence, Field Operations) to translate complex analytical insights into actionable strategies - Communicate findings, model performance, and strategic recommendations to senior leaders through written documents and presentations - Mentor junior data scientists and engineers on the team; raise the bar on scientific rigor and analytical best practices - Contribute to the team's multi-year science roadmap, identifying new opportunities to apply ML/AI (including Generative AI and agentic architectures) to operational challenges A day in the life Amazon Benefits: 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: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan

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.

The Alexa Daily Essentials team delivers experiences critical to how customers interact with Alexa as part of daily life. Alexa users engage with our products across experiences connected to Timers, Alarms, Calendars, Food, and News. Our experiences include critical time saving techniques, ad-supported news audio and video, and in-depth kitchen guidance aimed at serving the needs of the family from sunset to sundown. As a Data Scientist on our team, you'll work with complex data, develop statistical methodologies, and provide critical product insights that shape how we build and optimize our solutions. You will work closely with your Analytics and Applied Science teammates. You will build frameworks and mechanisms to scale data solutions across our organization. If you are passionate about redefining how AI can improves everyone's daily life, we’d love to hear from you. Key job responsibilities Key job responsibilities Problem-Solving - Analyze complex data to identify patterns, inform product decisions, and understand root causes of anomalies. - Develop analysis and modeling approaches to drive product and engineering actions to identify patterns, insights, and understand root causes of anomalies. Your solutions directly improve the customer experience. - Independently work with product partners to identify problems and opportunities. Apply a range of data science techniques and tools to solve these problems. Use data driven insights to inform product development. Work with cross-disciplinary teams to mechanize your solution into scalable and automated frameworks. Data Infrastructure - Build data pipelines, and identify novel data sources to leverage in analytical work - both from within Alexa and from cross Amazon - Acquire data by building the necessary SQL / ETL queries Communication - Excel at communicating complex ideas to technical and non-technical audiences. - Build relationships with stakeholders and counterparts. Work with stakeholders to translate causal insights into actionable recommendations - Force multiply the work of the team with data visualizations, presentations, and/or dashboards to drive awareness and adoption of data assets and product insights - Collaborate with cross-functional teams. Mentor teammates to foster a culture of continuous learning and development

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 their 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 leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. We are hiring an Economist on the team to develop the next generation of incrementality measurement products, capturing the effect of advertising in driving sales as well as the effects of measurement tools on advertiser engagement with Amazon. As an Economist on the team, you will lead the design, implementation, and validation of large-scale causal inference methodologies to capture these properties. You will communicate your results with science and business leaders, and partner with other scientists and engineers to carry solutions into production. Key job responsibilities Leverage deep expertise in causal inference to develop robust, causally grounded ads measurement solutions Disambiguate problems to propose clear evaluation frameworks and success criteria Work autonomously and write high quality technical documents 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 and leaders Contribute new ideas that shape the direction of the team's work Mentor more junior scientists and participate in the hiring process

In Amazon Advertising, we apply machine learning at massive scale to optimize the prediction, ranking, and bidding behind every ad — deciding, in milliseconds, which ads to show shoppers and how to value them. We're looking for an Applied Scientist to help make sure the ads shoppers see are the right ones for them. You'll work across the science of how we rank, value, and bid on ads for Amazon DSP (Amazon's Demand-Side Platform) — including how we judge whether an ad is a good fit for the page a shopper is on and for the shopper themselves. It's high-scale, low-latency, customer-facing science: your models run live in front of millions of shoppers under tight real-time constraints. The questions are genuinely open — how do you tell whether an ad is relevant to someone, how do you balance what's good for shoppers, advertisers, and Amazon, and how do you keep getting that right as shopping behavior and inventory shift underneath you? Your work will have real impact, and you'll have room to shape where we take it. A few things make this stand out: your models touch a huge share of the ads shoppers see every day, so even small improvements add up fast; you'll run modern ML live under strict latency limits, across regions and very different types of ad inventory; and the problem space is rich — from how we value and bid on ads, to keeping models stable as traffic shifts, to what makes an ad a good fit for a shopper. Key job responsibilities - Design and improve the models that decide how ads are ranked, valued, and priced — including how relevant an ad is to the page and the shopper. - Apply and extend state-of-the-art techniques across e.g. ranking, deep learning, and information retrieval. - Own problems end to end: frame them, prototype, experiment, and ship them to production. - Balance competing objectives — shopper experience, advertiser and publisher value, and Amazon's business — into models that hold up across placements and marketplaces. - Communicate your work clearly to both business and science audiences, tailoring how you share it to each. - Write and ship your own production code backed by strong engineering support — we're all builders here. - Move fast with the best tools available, including modern AI coding assistants and agents. A day in the life You might start by digging into last week's experiment results, then use an AI coding agent to get your next prototype built and ready to test in production. In the afternoon you could be sketching a new way to measure ad relevance, reading a recent paper that bears on it, and talking it through with a senior scientist on the team. You'll move between hands-on science, writing and shipping real production code, and making the calls on your own work. About the team We're a group of scientists and engineers based in Edinburgh and London, working to make Amazon's ads more performant and relevant. We sit within a larger team spread primarily across New York City and the UK, and we have a broad mandate to build and experiment. You'll work alongside senior applied scientists you can learn from, with the data and infrastructure to do the work well and room to grow — with opportunities to attend top conferences (e.g., NeurIPS, KDD, ICML) and take on more scope over time.

The Media Planning Science team develops and implements models that deliver insights and recommendations for strategic media planning and measurement across Amazon Advertising's product portfolio. Our mission is to help advertisers create and execute plans that meet their objectives while providing accurate measurement tools. We work on a multitude of problem statements that encompass Incremental Reach, Budget Planning Optimization, and Recommendations. Our models leverage both heuristic and machine learning approaches including deep learning techniques, with insights delivered through agent-based tools and APIs that integrate seamlessly into user interfaces and programmatic systems to ensure optimal advertising outcomes. As an Applied Scientist on the team, you will be at the forefront of innovation, developing media planning solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel insights to our customers. Key job responsibilities * Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement and media planning 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 junior scientists and participate in the hiring process A day in the life You will solve real-world problems by analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop ML/DL models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the advertising organization. You will prepare written and verbal documents to share insights to audiences of varying levels of technical sophistication. About the team We are a team of scientists across Applied and Data Science disciplines. You will work with colleagues with deep expertise in ML, DL, NLP, 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.

About us As part of the AWS Applied AI Solutions organization, our vision is to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers’ businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. Our team combines Amazon's real-world experience with state-of-art AI to create opinionated, turnkey solutions that are no-brainers to buy and easy to use. We're building applied AI solutions that businesses love and trust. Our ambition is to become the partner companies rely on to run their business every day—putting AI to work to deliver better customer experiences, operational excellence, and faster innovation. We're a fast-moving, scrappy team building a new agentic product from the ground up. If bias for action is your favorite leadership principle, you'll fit right in. The Role We're seeking a talented Senior Applied Scientist with expertise in large language models, agentic systems, and foundational models. You will be responsible for building the state-of-art multi-agent system, using a handful of methods including fine-tunning, reinforcement learning, etc. You'll accelerate our customer-facing features, contribute to our collaborative and innovative culture, and bring state-of-art applied research that raises the bar for the entire team. Key job responsibilities • Drive end-to-end GenAI projects with high complexity and ambiguity from conception to production • Build, optimize, and deploy ML models while collaborating with software engineers for productionization • Research innovative machine learning approaches and identify new opportunities for GenAI applications • Perform hands-on analysis and modeling of large datasets to develop actionable insights • Establish scalable, automated processes for data analysis, model development, and validation • Present results to senior leadership and collaborate with cross-functional teams About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.