Careers

Here's the job description with causal ML woven in: We are looking for a talented, organized, and customer-focused applied researcher to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon. This role requires an individual with exceptional machine learning modeling and architecture expertise — particularly in deep learning, neural networks, and transformer-based architectures applied to price prediction and forecasting problems. Equally important is deep expertise in causal machine learning — including causal inference, treatment-effect estimation, and experimentation methods (e.g., uplift modeling, double/debiased machine learning, instrumental variables, and A/B and quasi-experimental design) — to isolate the true impact of pricing and promotion decisions on customer behavior and business outcomes. The ideal candidate brings a strong foundation in applied statistics and probabilistic modeling, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment. Key job responsibilities See the big picture. Understand and influence the long-term vision for Amazon's science-based competitive, perception-preserving pricing techniques. Develop and advance price prediction models leveraging deep learning frameworks, transformer architectures, and advanced statistical methods to drive pricing accuracy at scale. Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale. Design and implement neural network-based architectures — including sequence models and transformers — for large-scale price prediction and optimization. Stay informed. Establish mechanisms to stay up to date on the latest scientific advancements in deep learning, transformer architectures, applied statistics, neural network design, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems. Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. Leverage statistical rigor and modern deep learning approaches to validate hypotheses and drive measurable pricing improvements. Successfully execute & deliver. Apply your exceptional technical machine learning expertise — including deep neural networks, attention-based models, and applied statistical analysis — to incrementally move the needle on some of our hardest pricing problems. A day in the life We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives. We drive cross-domain and cross-system improvements through: * shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. * Error detection and price quality guardrails at scale. * Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into Stores architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication. About the team The Pricing Optimization science group builds and refines Amazon's algorithmic pricing and promotion models at scale. Our team combines expertise in deep learning, transformer architectures, applied statistics, and probabilistic forecasting to develop price prediction systems that directly impact the customer experience. The team also brings hands-on experience with causal modeling and inference — including uplift modeling and treatment effect estimation — to rigorously measure the impact of pricing decisions on customer behavior and business outcomes. We partner closely with product, engineering, and business teams to take solutions from research through production deployment.

We are seeking a Senior Applied Scientist to join our team in developing pioneering AI research, Generative AI, Agentic AI, Large Language Models (LLMs), Diffusion and Flow Models, and other advanced Machine Learning and Deep Learning solutions for Amazon Selection and Catalog Systems, within the AI Lab Team. This role offers a unique opportunity to work on AI research and AI products that will shape the future of online shopping experiences. Our team operates at the forefront of AI research and development, working on challenges that directly impact millions of customers worldwide. We push the boundaries of AI at both the foundational and application layers. As a Senior Applied Scientist, you will have the chance to experiment with LLMs and deep learning techniques, apply your research to solve real-world problems at an unprecedented scale, and collaborate with experienced scientists to contribute to Amazon's scientific innovation. Join us in redefining the future of shopping. Your work will directly influence how customers interact with the world's largest online store. Key job responsibilities - Design and implement novel AI solutions for Amazon catalog of products - Develop and train state-of-the-art LLMs, Diffusion Models, and other Generative AI models - Build and deploy autonomous AI Agents in Amazon production ecosystem - Scale AI models to handle billions of diverse products across multiple languages and geographies - Conduct research in areas such as Autonomous AI Agents, Generative AI, Language Modeling, Multi-modality Computer Vision, Diffusion Models, Reinforcement Learning - Collaborate with cross-functional teams to integrate AI models into Amazon's production ecosystem - Contribute to the scientific community through publications and conference presentations

Alexa International is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs) and ASR, TTS, & Speech to Speech models, requiring foundational deep learning and generative models knowledge. Applied scientists will contribute to cross-team scientific efforts, collaborate with partner teams, and deliver solutions that impact Alexa's international products and services. Key job responsibilities As an Applied Scientist with the Alexa International team, you will work with talented peers to develop and implement algorithms and modeling techniques to advance the state of the art with LLMs, particularly contributing to scientific research and applied AI for multi-lingual applications — a challenging area for the industry globally. Your work will directly impact our global customers in the form of products and services that support Alexa+. You will leverage Amazon's heterogeneous data sources and large-scale computing resources to accelerate advances in text, speech, and vision domains. The ideal candidate possesses a foundational understanding of machine learning, speech and/or natural language processing, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in a fast-paced environment, like to tackle complex challenges, and are eager to deliver impactful solutions while iterating based on user feedback. A day in the life * Analyze, understand, and model customer behavior and the customer experience based on large-scale data. * Build and support online & offline evaluation metrics and methodologies for multimodal personal digital assistants. * Work on ASR, TTS, and Speech-to-Speech (S2S) model training and fine-tuning * Fine-tune/post-train LLMs using techniques like SFT, DPO, Reinforcement Learning (RLHF and RLAIF) for supporting model performance specific to a customer's location and language. * Experiment and help set up experimentation frameworks for agile model and data analysis or A/B testing. * Contribute to research efforts that drive innovation forward. * Collaborate with cross-team scientists and engineers on LLM evaluation frameworks, post-training methodologies, and best practices for international speech and language systems. * Contribute to end-to-end delivery of scientific solutions from research to production, including reusable science components and services. * Communicate solutions clearly to peers, partners, and stakeholders. * Actively participate in the broader internal and external scientific community through publications and community engagement.

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. We are seeking a highly skilled and analytical Data Scientist. You will play an integral part in the measurement and optimization of Amazon Music marketing activities. You will have the opportunity to work with a rich marketing dataset together with the marketing managers. This role will focus on developing and implementing models that aids in audience segmentation, AI-enablement in data/reporting, and assessing randomized controlled trials to rate marketing effectiveness. This role is suitable for candidates with strong background in cohort analysis, causal inference, statistical analysis, and data-driven problem-solving, with the ability to translate complex data into actionable insights. As a key member of our team, you will work closely with cross-functional partners to optimize marketing strategies and drive business growth. Key job responsibilities Develop Causal & Predictive Models Build and validate causal and predictive models that quantify how marketing and lifecycle programs affect customer retention, engagement, and subscriber growth for Amazon Music. Own the team's models end to end. Statistical Analysis at Scale Analyze large customer datasets using SQL and Python or R to interpret results and uncover meaningful patterns, grounding your work in trusted, well-governed data. Enable Data-Driven Decisions Partner with marketing and finance stakeholders to deliver recommendations that improve retention and return on investment. Prioritize the work with the greatest impact on customer growth, and present findings clearly to both technical and executive audiences. Bring AI into Self-Service Tools Partner with product managers and engineers to incorporate AI and machine learning into the team's self-service analytics tools, so stakeholders can answer their own questions faster and at greater scale. Cross-Functional Problem Solving Work across marketing, product, and engineering teams to frame key business questions and build credible analytical solutions. Innovate & Document Track emerging methods and apply them to improve measurement; maintain reproducible documentation and clear reporting for non-technical audiences.

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 mission in International Seller Services (ISS) is to provide technology solutions for improving the seller and customer experience, drive seller compliance, maximize seller success, and improve internal workforce productivity. Team's main focus is to build products that are scalable across different regions of the world, while working in partnership with ISS regional stakeholders and multiple partner teams across Amazon. As a Data Scientist, you will be responsible for modeling complex problems, discovering insights, and building risk algorithms that identify opportunities through statistical models, machine learning, and visualization techniques to improve operational efficiency. As a Data Scientist, you will leverage your expertise in Machine Learning, Natural Language Processing (NLP), and Large Language Models (LLM) to develop innovative solutions for Amazon's ISS team. You'll be responsible for modeling complex problems, building innovative algorithms, and discovering actionable insights through statistical models and visualization techniques to enhance operational efficiency in the e-commerce space. The role combines usage of latest AI technology with practical business applications, requiring someone passionate about transforming the way we interact with technology while delivering measurable impact through advanced analytics and machine learning solutions. You will need to collaborate effectively with business and product leaders within ISS and cross-functional teams to build scalable solutions against high organizational standards. The candidate should be able to apply a breadth of tools, data sources, and Data Science techniques to answer a wide range of high-impact business questions and proactively present new insights in concise and effective manner. The candidate should be an effective communicator capable of independently driving issues to resolution and communicating insights to non-technical audiences. This is a high impact role with goals that directly impacts the bottom line of the business. Key job responsibilities Analyze terabytes of data to define and deliver on complex analytical deep dives to unlock insights and build scalable solutions through Data Science to ensure security of Amazon’s platform and transactions Build Machine Learning and/or statistical models that evaluate the transaction legitimacy and track impact over time Ensure data quality throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, transformation, and cross-lingual alignment/mapping Define and conduct experiments to validate/reject hypotheses, and communicate insights and recommendations to Product and Tech teams Develop efficient data querying infrastructure for both offline and online use cases Collaborate with cross-functional teams from multidisciplinary science, engineering and business backgrounds to enhance current automation processes Learn and understand a broad range of Amazon’s data resources and know when, how, and which to use and which not to use. Maintain technical document and communicate results to diverse audiences with effective writing, visualizations, and presentations

How does Amazon decide which fulfillment center ships your order, which truck carries it, and how to keep promise, across hundreds of millions of packages daily? SCOT Fulfillment Optimization (FO) owns the science behind these decisions. We are seeking Applied Scientists to join the FO Science & Tech team in Luxembourg (alternatively: Barcelona, or London). You will design and build optimization models that power Amazon's fulfillment decisions at scale from real-time order assignment and multi-objective cost-speed tradeoffs to capacity-aware control systems that steer millions of shipments per hour toward operational plans. Basic qualifications * PhD in Operations Research, Applied Mathematics, Computer Science, or related field (or equivalent experience) * Strong programming skills (Python preferred; experience with optimization solvers a plus) * Research experience in one or more: * Large-scale mathematical programming (LP, MIP, decomposition methods) * Combinatorial optimization (assignment, scheduling, network flows) * Multi-objective optimization and control Preferred qualifications * Experience building optimization systems that run in production at scale * Being comfortable with ambiguity and fast iteration cycles * Publications in relevant venues Key job responsibilities Design and implement optimization models (MIP, heuristics, decomposition) that solve large-scale fulfillment problems, from order assignment to network flow control. Build research prototypes end-to-end: from problem formulation through scalable implementation to production validation. Analyse complex tradeoffs (cost, speed, capacity) and translate findings into actionable recommendations for leadership and operations teams. Collaborate with engineers to bring science solutions into production systems serving millions of customer orders daily. A day in the life You formulate an optimization problem on a whiteboard with teammates, then prototype it in Python with real data by the afternoon. You run experiments against production-scale datasets, iterate on the model, and present results to stakeholders who will use them to make network decisions next week. Some days you dive deep into solver performance; other days you're explaining a Pareto frontier to an operations leader. You collaborate with large engineering and product teams to bring your solutions into systems serving millions of customers. Alongside fast-turnaround prototypes, you own long-term research bets, the kind that reshape how Amazon's fulfillment network operates at scale. Your work goes live. About the team SCOT Fulfillment Optimization Science & Tech (FO SnT) is the applied research team behind Amazon's fulfillment decision-making systems. We decide how orders get assigned to warehouses, how capacity is allocated across the network, and how cost and speed tradeoffs are managed in real time, at global scale. Our models influence billions of euros in annual operational spend. They protect sites from overload during peak, reduce transportation costs and CO2 emissions, and ensure customers receive their packages when promised. Leadership relies on our science to make investment decisions worth hundreds of millions. We are practitioners of large-scale optimization: MIP formulations, decomposition methods, approximation algorithms, and parallelisation. We use machine learning where it sharpens our decisions, including forecasting, learned heuristics, and multi-armed bandits. We pick the right tool for the problem, not the fashionable one. You will work alongside Senior and Principal scientists, and collaborate with Amazon Scholars and academic partners who bring frontier research into our applied problems. We code our prototypes to be production-ready and collaborate with large engineering teams to ship systems, not papers. Above all, we have fun solving hard real-world problems at real-world speed, failing, learning, and shipping along the way.

Do you thrive in generating actionable insights at scale from complex datasets? Can you use data to look around the corners of a business and guide leaders on impactful business decisions? The Shop and Keep Data and Science team builds internet-scale shopping intelligence that helps Amazon customers find what they want with ease and give them the right information to make confident and lasting purchases. Our work powers customer experiences across Amazon.com surfaces, including Search, Product Detail Pages, Recommendations and Ads. We are looking for an experienced Senior Data Scientist to lead high visibility science initiatives that guide the business and ensure quality of our shopping intelligence signals. This role will focus on Data Science and Analytics related to signal quality evaluation, impact measurement, and delivering business insights. The role requires deep technical skills, strong business acumen and a deep analytical background to provide actionable data-driven insights and decision support. As a member of this team, the role will dive deep into our data and leverage their strong skills in measurement science, LLMs, predictive and prescriptive models, data ETLs, dashboards and visualization to support business strategy and deliver goals with executive visibility. You must have the ability to communicate effectively across multiple technical and non-technical business units, as well as across other geographies. Successful members of this team collaborate effectively to solve data problems, are highly organized and detail-oriented, implement new solutions for complex problems, and deliver successfully against highest standards. The ideal candidate will be a highly motivated individual that seeks to “tell the story” of the data with little direction or supervision. Key job responsibilities • Leverage state-of-the-art measurement science approaches, including LLMs, to develop evaluation frameworks for a variety of signals and customer experiences. • Support the development of continuously-evolving business analytics and data models, own the quantitative analysis of the performance of our features. • Continually develop new ways of using data to look around the corners and guide the development of shopping intelligence signals. • Use LLMs, machine learning, data mining, and statistical techniques to design/run experiments that solve complex business problems. • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. • Develop a deep understanding of e-commerce metrics, reporting tools, and data structures in order to identify and drive resolution of issues, provide actionable intelligence with existing metrics or identify, develop, and propose new metrics, dashboards, scorecards or new tools. • Develop relationships and processes with partner teams, PMs/TPMs, engineers, and other functional teams to identify and address reporting issues. • Manage and develop advanced analytical tools that align, and simplify, monthly business reviews, annual planning, operations and forecasting processes, based on the needs of the business and stakeholders. About the team The Shop and Keep Data and Science team's mission is to build shopping intelligence that helps Amazon customers find what they want with ease and give them the right information to make confident and lasting purchases. Through our personalized discovery and product evaluation signals, we assist customers in their shopping missions as they navigate Amazon.com's surfaces. We also share a selection of signals with partner teams and sellers to establish closed loop mechanisms and drive improvements in our signals, customer outcomes and Amazon’s product selection.

Are you interested in big data, Machine Learning, and building recommendation services using Generative AI? If so, Amazon's Personalization team might be the right place for you. Key job responsibilities - Use AI and machine learning to create scalable solutions for business problems. - Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes. - Design, develop and evaluate highly scalable models for predictive learning. - Work closely with software engineering teams to drive model implementations and new feature creations. - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Research and implement novel machine learning and statistical approaches - Review peer work and provide feedback A day in the life You are an Applied Scientist who loves big data and passionate about improving customer shopping experience by inventing and applying state-of-art technologies (e.g., LLMs/SLMs, Machine Learning, Natural Language Processing, and Computer Vision) to build the next-generation product recommendation engine for Amazon. You have an entrepreneurial spirit, know how to deliver, are deeply technical and highly innovative. You work closely with software engineers to put algorithms into production. You also work in partnership with teams across Amazon to create enormous benefits for our customers. About the team We are part of Amazon’s Personalization organization, a high-performing group with a huge impact on hundreds of millions of customers, innovating at the intersection of customer experience, machine learning, and large-scale distributed systems. We run global experiments and our work has revolutionized e-commerce with features such as "Compare with similar items", "Keep Shopping For", “Customers who bought this item also bought”, and, “Frequently bought together” among others.

We are seeking a seasoned executive leader to join our Network Solutions team within Amazon Customer Service, as Director, Data Science. In this role you will build and lead a data, analytics, and measurement science function that orchestrates Amazon's Customer Service full network – across our human-assisted and customer-facing automation AI-enabled channels – as a single intelligent system. Network Solutions owns demand forecasting, network planning, routing, real-time observability, and workforce strategy, and is building the next generation of AI-enabled planning systems with intelligent and adaptive capabilities that consider humans and AI-agents, simultaneously. You will lead a multi-disciplinary team – scientists, data engineers, business intelligence engineers, and analysts – who will build the data strategy these new systems require and the measurement science that reshapes how every network decision is made. At the core is a unified measurement framework that measures the value of experiences across customers, associates, and our business, drawing on causal inference, economics, operations, and behavioral science. You will create the underlying logic for how we plan capacity, route customers, develop associates, and allocate resources – and serve as an interface and work closely with partner teams within Customer Service, including the CS-wide Data Intelligence organization. The ideal candidate brings deep cross-disciplinary experience in data science, measurement, and operations – a track record of building and leading data and insights organizations from 0-to-1, the scientific depth to drive novel measurement frameworks, and the rare ability to blend quantitative rigor with an understanding of human systems – developing the right logic to drive decision-making at scale. Key job responsibilities - Build and lead the Network Solutions data and analytics organization: provide unified leadership across business intelligence engineering, data engineering, analytics, and reporting; recruit, grow, and retain a team spanning measurement scientists, data engineers, and analysts, building the function from 0-to-1 - Drive the measurement science and decisioning core: develop the causal inference-based framework that quantifies value creation across multiple dimensions; establish methodology standards, signal definitions, and utility function design that give Network Solutions a unified view of performance - Own the Network Solutions data foundation: own critical signal pipelines, govern data quality, and build a composable data architecture that all Network Solutions product and science teams build on; ensure measurements are interoperable across planning, routing, incident management, and workforce systems - Integrate measurement science into production systems: demonstrate the shift to value-informed decision-making; partner with product and engineering teams to embed measurement frameworks into planning, routing, and resource allocation - Interface with Amazon CS and company-wide data, analytics, and science organizations: represent Network solutions as a peer partner to central data infrastructure, customer experience measurement, and applied science functions; consolidate Network Solutions data demand into a clear, prioritized voice - Drive adoption of data standards and measurement frameworks: translate complex models and multi-dimensional utility functions into intuitive, actionable insights for non-technical stakeholders; establish shared measurement standards that teams across the organization can build on - Develop the long-term data and science roadmap: anticipate future needs as the organization scales; identify opportunities to extend measurement capabilities beyond Network Solutions About the team Network Solutions sits at the intersection of customer experience and associate experience within Amazon Customer Service, owning the logic and infrastructure that determines how those two sides are balanced, served, and optimized together. On one side is the customer: their experience, their journey, and the AI automation that increasingly shapes both. On the other side is the associate: their work, their well-being, and the conditions that make them effective. Network Solutions sits at the center, owning demand forecasting, network planning, routing and matching, real-time observability, and workforce strategy – including the long-term design of the network itself as the balance between AI and human engagement continues to evolve. We are building the next generation of systems that will transform how Customer Service operates using intelligent, adaptive capabilities that learn and improve continuously.

Amazon's Search team creates ML algorithms that connect customers around the world with products that delight them. We harness machine learning at Amazon's scale to make the customer experience easier and smoother. Our impact is large. For example, if your innovations save even 1 minute per customer per year, then for every 100 million customers, you save approximately 190 years of human effort. Key job responsibilities As an Applied Scientist on the Search Ranking team, you will build search ranking models that work for thousands of product types, billions of queries, and hundreds of millions of customers spread around the world. You will find the next set of big improvements to ranking, leverage large datasets to understand the complexities of customer behavior, and build ML models that work at Amazon scale. Amazon's Search ranking relies on efficient early stage ranking followed by power final stage ranking models. this role focuses on efficient models for early stage ranking phase, and techniques to personalize these models and optimize them automatically. A day in the life Our primary focus is improving search ranking systems. On a day-to-day this means building ML models, analyzing data from your recent A/B tests, and collaborating with partner teams on joint goals. We leverage agentic workflows to improve our scientific velocity, and partner closely with engineering teams to bring our ideas to production. You will shape the future of search ranking by presenting proposals to Amazon's business and tech leaders. About the team We are a team consisting of ML scientists and software engineers. Our interests span machine learning for better ranking, personalization to refine the ranking of our models, reinforcement learning to bring the benefits of exploration to search ranking, and infrastructure to make it all happen at scale and efficiently.