Careers

Esta é uma posição de colaborador individual, com base em nosso escritório de São Paulo. Procuramos uma pessoa dinâmica, analítica, inovadora, orientada para a prática e com foco inabalável no cliente. Na Amazon, nosso objetivo é exceder as expectativas dos clientes, garantindo que seus pedidos sejam entregues com máxima rapidez, precisão e eficiência de custo. A determinação da rota de cada pacote é realizada por sistemas complexos, que precisam acompanhar o crescimento acelerado e a complexidade da malha logística no Brasil. Diante desse cenário, a equipe de Otimização de Supply Chain está à procura de um cientista de dados experiente, capaz de desenvolver modelos, ferramentas e processos para garantir confiabilidade, agilidade, eficiência de custos e a melhor utilização dos ativos. O candidato ideal terá sólidas habilidades quantitativas e experiência com conjuntos de dados complexos, sendo capaz de identificar tendências, inovar processos e tomar decisões baseadas em dados, considerando a cadeia de suprimentos de ponta a ponta. Key job responsibilities * Executar projetos de melhoria contínua na malha logística, aproveitando boas práticas de outros países e/ou desenvolvendo novos modelos. * Desenvolver modelos de otimização e cenários para planejamentos logísticos. * Criar modelos de otimização voltados para a execução de eventos e períodos de alta demanda. Automatizar processos manuais para melhorar a produtividade da equipe. * Auditar operações, configurações sistêmicas e processos que possam impactar custos, produtividade e velocidade de entregas. * Realizar benchmarks com outros países para identificar melhores práticas e processos avançados, conectando-os às operações no Brasil. About the team Nosso time é composto por engenheiros de dados, gerentes de projetos e cientistas de dados, todos dedicados a criar soluções escaláveis e inovadoras que suportem e otimizem as operações logísticas da Amazon no Brasil. Nossa missão é garantir a eficiência de todas as etapas da cadeia de suprimentos, desde a primeira até a última milha, ajudando a Amazon a entregar resultados com agilidade, precisão e a um custo competitivo, especialmente em um ambiente de rápido crescimento e complexidade.

Are you interested in working with top talents in Optimization, Operations Research and Supply Chain to help Amazon to efficiently match our Devices with worldwide customers? We have challenging problems and need your innovative solutions to make tremendous financial impacts! The Amazon Demand Science Optimization organization is looking for an Applied Scientist with background in Operations Research, Optimization, Supply Chain, Simulation, and Gen AI to support science efforts to integrate across inventory management functionalities. Our team is responsible for science models (both deterministic and stochastic) that power world-wide inventory allocation, promotion optimization for Amazon Devices business that includes Echo, Kindle, Fire Tablets, Amazon TVs, Amazon Fire TV sticks, Ring, and other smart home devices. We formulate and solve challenging large-scale financially-based optimization problems which ingest demand forecasts and produce optimal price promotion strategies, procurement, production, distribution, and inventory management plans. In addition, we also work closely with the demand forecasting, material procurement, production planning, finance, and logistics teams to co-optimize the inventory management and supply chain for Amazon Devices given operational constraints. Key job responsibilities The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail, and an ability to work in a fast-paced and ever-changing environment and a desire to help shape the overall business. Responsibilities include: - Design and develop advanced mathematical, simulation, and optimization models and apply them to define strategic and tactical needs and drive appropriate business and technical solutions in the areas of inventory management and distribution, network flow, supply chain optimization, and demand planning - Apply mathematical optimization techniques (linear, quadratic, SOCP, robust, stochastic, dynamic, mixed-integer programming, network flows, nonlinear, nonconvex programming) and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software - Research, prototype and experiment with these models by using modeling languages such as Python; participate in the production level deployment - Create, enhance, and maintain technical documentation, and present to other Scientists, Product, and Engineering teams - Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans - Influence the organization's long-term roadmap and resourcing, onboard new technologies onto Science team's toolbox, mentor other Scientists About the team Amazon Science https://www.linkedin.com/showcase/amazonscience/posts/?feedView=all

We are seeking a stellar Data scientist who has experience developing science products, drive conversations with business stakeholders and visible customer impact. We would prefer if your previous work has been in scalable Agentic, RL or forecasting products. Strong academic background in Statistics, Machine Learning & Science is required with white paper publications/science cast studies about your work being a plus. • Master’s degree in statistics, CS or ML related fields • Scientist/Tech Lead creating and shipping impactful ML products. • Ability to write clear, structured and modularized scripts in Python. • Expertise in ML & Deep Learning frameworks such as Tensorflow, Keras and Pytorch & Agentic frameworks such as LangChain, Crew AI etc. • Industry experience working with complex AI systems. • Experience and technical expertise across various science domains. Crucial ones being statistics, deep & machine learning. • Experience creating data pipelines & proficient in querying data from Spark/HIVE/Redshift/other large scale data warehousing platforms. • Expert in distilling informal customer requirements into problem definitions, dealing with ambiguity and formulating ML products to solve these problems. Key job responsibilities In this position, you will be a key contributor (with direct leadership visibility) building, productionizing (real & batch) and measuring impact of state of the art personalized Gen AI systems for Amazon global selling partners and contribute to Amazon wide research in this area in the form of publications and white papers. You will work with global leaders and teams across time zones on a regular basis. About the team Millions of Sellers list their products for sale on the Amazon Marketplace. Sellers are a critical part of Amazon’s ecosystem to deliver on our vision of offering the Earth’s largest selection and lowest prices. In this ecosystem our team plays a critical role in enabling Sellers across EU5, China, Japan, Australia, Brazil and Turkey to make their Selection available to customers globally and deliver the experience they have come to expect from Amazon. We help independent sellers compete against our first-party business by investing in and offering them the very best selling tools we could imagine and build. We are pushing the boundaries of these machine learning tools in areas of Agentic, recommendation and forecasting systems to help our sellers sell more and across borders.

We are looking for researchers who aim to build super-intelligent AI systems that leverage proof assistants to guide learning and reasoning. Our neuro-symbolic AI technology is applied across a wide range of science and engineering domains within Amazon, and you will join the team at the forefront of this research. As an Applied Scientist here, you will play a pivotal role in shaping the definition, vision, and development of product features from beginning to end. You will: * Define and implement new neuro-symbolic applications that employ scalable and efficient approaches to solve complex problems. * Work in an agile, startup-like development environment, where you are always working on the most important stuff. * Deliver high-quality scientific artifacts. About the team We work closely with academia. Our team includes an Amazon Scholar in mathematics, and we maintain active research collaborations with faculty at leading CS departments (MIT, Berkeley, CMU). 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 Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. 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 in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Data Scientist III Job Location: San Diego, California Job Number: AMZ9803634 Position Responsibilities: Own the data science elements of various products to help with data-based decision making, product performance optimization, and product performance tracking. Work directly with product managers to help drive the design of the product. Work with Technical Product Managers to help drive the build planning. Translate business problems and products into data requirements and metrics. Initiate the design, development, and implementation of scientific analysis projects or deliverables. Own the analysis, modelling, system design, and development of data science solutions for products. Write documents and make presentations that explain model/analysis results to the business. Bridge the degree of uncertainty in both problem definition and data scientific solution approaches. Build consensus on data, metrics, and analysis to drive business and system strategy. 40 hours / week, 8:00am-5:00pm, Salary Range $159,200/year to $215,300/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000

Do you want to help shape the future of Amazon's physical retail presence? Worldwide Grocery Stores (WWGS), Location Strategy and Analytics team is looking for an Research Scientist to join us in developing advanced forecasting models, optimization models, and analytical tools to support critical real estate and store planning decisions for Amazon's Worldwide Grocery business, including Whole Foods Market. Our team is responsible for developing predictive models and tools to support Real Estate and Topology analysts in making important decisions regarding our stores—including new store openings, relocations, closures, remodels, design, new formats, and more. We leverage statistical modeling, machine learning, and GenAI to build solutions for store sales forecasting, sales transfer effects, macrospace optimization, store network optimization, store network diffusion planning, and causal effects. As a Research Scientist on our team, you will apply your technical and analytical skills to tackle complex business problems and develop innovative solutions to improve our forecasting and decision-making capabilities. You will collaborate with a diverse team of scientists, economists, and business partners to identify opportunities, develop hypotheses, build internal products, and translate analytical insights into actionable recommendations for Executive Leadership. Key job responsibilities - Design and implement forecasting models and machine learning solutions to predict store performance and optimize our retail network. - Analyze large datasets to uncover insights and patterns related to store performance, customer behavior, and market dynamics. - Develop end-to-end solutions, tools and frameworks to scale our ML model development and data analysis. - Leverage GenAI models to enhance user interaction with our solutions, improve overall user experience, and build new features. - Present research findings and recommendations to scientists, business leaders, and executives. - Collaborate with cross-functional teams to drive adoption of models and insights. - Stay current on latest developments in relevant fields and propose innovative approaches. About the team We are a team of scientists passionate about leveraging data and advanced analytics to drive strategic decisions for Amazon's grocery business. Our work directly impacts Amazon's worldwide grocery store growth and development strategy. We foster a collaborative environment where team members are encouraged to think creatively, challenge assumptions, and pursue novel approaches to solving complex problems. Our team is at the forefront of applying a multitude of techniques - including GenAI - to improve our scientific solutions and products.

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 Principal Data Scientist to develop next-gen measurement and experimentation systems within Prime Video Personalization and Discovery. You'll be part of an embedded science 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 building experiment-based measurement systems at scale, 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 - Define and drive the multi-year vision for experiment-based measurement systems within 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 and review technical artifacts to ensure quality - Stay up-to-date on the latest data science tools, techniques, and best practices and help evangelize them across the organization

As part of the AI Operations Integration team, we're passionate about pushing the boundaries of AI and transforming how operations teams work. We are looking for an entrepreneurial, experienced, creative, and AI-Native Data Scientist I to join our team. As a Data Scientist I on the AI Operations Integration team, you'll have the opportunity to work on exciting, ambiguous problems that combine Large Language Models (LLMs), Generative AI, and predictive analytics to create intelligent, data-driven operational solutions that fundamentally change how work gets done across Amazon's global operations footprint. You will be responsible for leading the development and delivery of core data science capabilities that power AI-enabled operations. You will have significant influence on our overall strategy by defining analytical approaches, driving solution architecture, and spearheading the data science best practices that enable a high-quality, scalable AI ecosystem. In this role, you'll collaborate with a diverse team of software engineers, AI/ML specialists, operations experts, and technical program managers to develop novel solutions that advance the state of the art in AI-enabled operations. You'll leverage Amazon's vast data resources and computing infrastructure to accelerate development and drive innovation. Your contributions will help define our overall data science strategy, from data enrichment and model optimization to system architecture and best practices, creating a virtuous cycle of AI-enablement that continuously improves operational excellence. Key job responsibilities - Assess and select ideal solution approaches from a wide range of data science methodologies, including machine learning, statistical modeling, NLP, and LLM-based techniques, to solve complex, ambiguous operational problems with significant business impact. - Apply deep expertise to problems involving complex interactions among software systems, data pipelines, and operational processes; design solutions that accurately model these interactions and are extensible, actionable, and easy for others to contribute to. - Own and deliver end-to-end data science solutions for the business with minimal assistance, building a track record of successful launches that drive measurable operational improvements across Amazon's global footprint. - Work closely with operations business teams to deeply understand their challenges, translate ambiguous needs into well-defined problem statements, and ensure data science solutions are grounded in real operational context. - Stay current on data science developments and emerging research; raise awareness of new and well-established techniques across the team - Partner with engineering and AI/ML teams to integrate data science solutions into existing operational systems; contribute to strategic planning (OP1/QBR/MBR) and advise senior leadership on AI investment priorities and data science strategy. A day in the life You start your morning with a profitability puzzle. Thousands of low-price products are losing money, and no single team can explain why. The buying, placement, and fulfillment systems each say they did the right thing, but the customer's order still ships in three boxes from three warehouses. You trace decisions across systems, find that a parameter was quietly misconfigured weeks ago, and write up the evidence chain. A couple times a week, you join a cross-team working session where scientists, engineers, and data teams collaborate on end-to-end investigations. You're connecting the dots across systems that don't normally talk to each other tracing a product from purchase order to customer doorstep and pinpointing where value leaks. Some cases have obvious fixes. The more interesting ones are where every system worked as designed but the outcome is still bad. On other days you might build a counterfactual simulation to test whether a different optimization approach would change the economics, design an A/B test to validate it, or present findings to leadership walking them through what you know, what you don't, and what level of confidence each finding carries. About the team We're part of a broader organization transforming how global operations teams work through AI. Within that mission, our team focuses on the hardest diagnostic problems: when automated supply chain systems produce bad outcomes and no single team can explain why. We build decision intelligence platforms that traces decisions across automated systems and uses causal engines and AI to find root causes. You'll work alongside scientists, SDEs, and ML engineers, and collaborate regularly with cross-functional partner SMEs. The team is new and you'd help shape it from the ground up.

Have you ever ordered a product on Amazon and when that box with the smile arrived, wondered how it got to you so fast? Wondered where it came from and how much it cost Amazon? If so, the Amazon Global Supply Chain Optimization Technology (SCOT) organization is for you. Watch this video to learn more about our organization, SCOT: http://bit.ly/amazon-scot We are the Optimal Sourcing Systems team (OSS) within SCOT and are looking for a Data Scientist II to join us! OSS designs and builds systems that measure and manage Amazon’s supplier capabilities, identify and react to supply disruptions, and prioritizes inbound freight for our global network. OSS software is used by every country Amazon services, and is a critical link to ensuring Amazon offers the products our customers want, at the lowest possible cost. This team under OSS orchestrates and tracks inventory movement into Amazon's network, maintains performance feedback loops, and ensures vendor compliance. The Data Scientist II, in partnership with the Product Management, Operations, and Tech teams, will lead efforts in four areas: 1) Building models to set optimal parameters such as lead times to ensure the accuracy of our Inbound network 2) Building analytical frameworks to identify and drive improvements in purchase order lifecycle management and defect coaching/chargebacks 3) Developing Gen AI solutions related to dispute evaluation and vendor coaching 4) Building models and solutions to enable collaborative inventory planning with vendors The ideal candidate thrives in ambiguous problem spaces, relishes working with large volumes of data, and enjoys the challenge of highly complex supply chain contexts. They can translate complex business logic into scalable models and communicate insights effectively to both technical and non-technical stakeholders. Keys to success in this role include exceptional analytics, statistics, judgment, and communication skills. Experience with supply chain optimization, operations research, or vendor management systems is a plus. Key job responsibilities - Collaborate with product managers, science, and engineering teams to design and implement model solutions for Sourcing Execution & Performance systems - Use large datasets or experiments to make causal inferences or predictions - Work with engineers to automate science analysis processes and build scalable measurement solutions - Interpret data, write reports, and make actionable recommendations - Drive technical standards and best practices for the team's Science solutions - Mentor and provide technical guidance to other team members on complex projects 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!

RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.