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MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Data Scientist III Job Location: Sunnyvale, California Job Number: AMZ10087624 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: $183,000/year to $247,600/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

Some CX shopping defects might be straightforward to detect and track. The interesting ones are not because they depend on what a customer perceives. For example, a search page may return legitimately different results, yet a shopper has no way to tell apart. We turn these ambiguous perception questions into a measurable artifact using LLMs, and we build the frameworks to know exactly where model judgment can be trusted and where a human must decide and proving it, against ground truth, at Amazon scale. This is one example of an LLM based measurement pipeline you will own, but that’s not all. You will extend that measurement to other parts of the shopping experience like the homepage and the detail page, where the same customer problem looks nothing like it does in search results, and where you will design the measurement from scratch. The larger goal is what makes this role unusual. Teams across Amazon are each independently figuring out how to label quality with LLMs, hitting the same problems alone: prompts that break on the next model version; golden sets nobody audited, accuracy that collapses in other locales. Through the work above, you will set the standard and build the production tooling behind it. Reusable labeling pipelines, evaluation frameworks, and inference infrastructure that hold up against Amazon-sized data and get adopted by teams who did not have to use them. Key job responsibilities - Design and improve LLM-based labeling for perception-driven defects: prompt design, sampling strategy, and the split between model judgment and human annotation. - Validate labeling quality against human ground truth, and build and maintain the golden datasets that make that validation possible. - Extend perceived-duplicate measurement to other parts of the shopping experience, designing the methodology where none exists and evaluating approaches already in use where one does. - Build reusable, production-grade labeling and evaluation tooling, batch inference, quality sampling, prompt and model version control, that operates on Amazon-scale data. - Define and publish the standards other teams adopt for using LLMs to measure customer experience. - Partner with science, engineering, and product teams across Stores to make quality measurement usable in their decisions, and present metric results and methodology changes to stakeholders. A day in the life We are a small team, which means your work is visible and your scope grows as fast as you do. There are existing partnerships with other teams and a paved way to cross org influence.

Every day, hundreds of millions of customers arrive on Amazon's Homepage, search for a product, and land on a detail page. Along the way, some of what we show them is irrelevant — a recommendation that misreads what they want, a widget that repeats what they just saw, a headline that doesn't say what it means, a product that shouldn't have been surfaced at all. We have built the ability to detect these defects at scale using Large Language Models (LLMs), and we report them to Amazon's most senior leadership as a company-level measure of shopping quality. What we have not yet built is the confidence to act on every one of them. That is the problem you will own. Today, the most consequential categories of shopping defects — product quality, duplication, staleness, irrelevance — go largely unenforced. Not because we cannot detect them, but because we have not yet clearly defined when they lead to customer dissatisfaction. These are genuinely hard questions. When is a recommendation irrelevant rather than merely unexpected? When are two products duplicates rather than legitimate alternatives? Every answer carries consequences for customers and advertisers. As Principal Data Scientist, you will own the analytical rigor behind Amazon's store quality metrics end-to-end: how they are defined, how faithfully our LLM implementations execute those definitions, and how accurate the resulting judgments actually are across relevance, presentation, product quality, and duplication. You will go deep enough to inspect individual model judgments and read the edge cases where they break, then come back up to argue definitional questions with the leaders who own the outcomes on both sides. You will also define experiments that will turn judgement into facts backed by data. Your influence will come from deep dives so well-constructed that stakeholders across two large organizations change their minds. Getting there means building the tooling that makes rigor repeatable rather than heroic: agents that walk the store the way a customer would, automated inspection of experiments, and feedback loops that finally connect what customers tell us directly to the metrics we optimize. Much of the customer voice we already collect goes underused today; you will change that. You will begin on the Homepage, where measurement is most mature, then extend the methodology to Detail Page and Search — carrying not just the metrics but the standard of evidence with them. Key job responsibilities - Validate store quality metrics end-to-end: inspect metric definitions, audit LLM implementations, and evaluate LLM judgment accuracy across quality dimensions (relevance, presentation, product quality, duplicates) - Starting with Homepage, extending cross-page — identify gaps and inconsistencies between Organic and Ads treatments that block the tiered enforcement framework - Drive alignment through deep dives that present evidence to both Stores and Ads stakeholders - Build and deliver deep-dive tooling (walk-the-store bots, automated experiment inspection via Gecko, VoC feedback loops) that make metric validation repeatable and self-serve - Own the analytical rigor behind the alignment on definitions of subjective/debated metrics and moving them to aligned/enforceable About the team Core Shopping Data Science owns the measurement of shopping quality across Amazon's Core Shopping eexperiences, including defect metrics reviewed by Amazon's most senior leadership. We focus on the long term and big picture to ensure that the full Amazon shopping experience balances strategic trade-offs. We work across Stores and Advertising, partnering with personalization, ranking, and search teams to turn measurement into changes customers can feel.

The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines. As an Applied Science Manager on the team, you will lead a team of scientists to define and execute a transformative vision for holistic catalog quality measurement, metadata enrichment, and content integrity. Your team will own the science solutions for foundational quality detection frameworks, metadata validation and correction technologies, state-of-the-art algorithms to identify and resolve catalog anomalies (violative content, duplicative/low value content, misattributed tracks, incorrect metadata), and/or agentic AI solutions that help internal teams quickly surface and fix quality issues to ensure customers receive accurate, complete catalog experiences. Key job responsibilities You independently manage a team of scientists. You identify the needs of your team and effectively grow, hire, and promote scientists to maintain a high-performing team. You have a broad understanding of scientific techniques, several of which may fall out of your specific job function. You define the strategic vision for your team. You establish a roadmap and successfully deliver scientific solutions that innovate on catalog quality detection, metadata enrichment, and content integrity. You define clear goals for your team and effectively prioritize, balancing short-term quality improvements and long-term innovation in catalog intelligence. You establish clear and effective metrics and scientific process to enforce consistent, high-quality artifact delivery and measurable catalog quality improvements. You proactively identify risks and bring them to the attention of your manager, customers, and stakeholders with plans for mitigation before they become roadblocks. You know when to escalate. You communicate ideas effectively, both verbally and in writing, to all types of audiences. You author strategic documentation for your team. You communicate issues and options with leaders in such a way that facilitates understanding and that leads to a decision. You work successfully with customers, leaders, and engineering teams. You foster a constructive dialogue, harmonize discordant views, and lead the resolution of contentious issues. About the team We are a team of scientists and MLEs focused on music catalog quality and metadata intelligence. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and data quality systems with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, content operations teams, and other scientists with expertise in music metadata, content classification, and building scalable modeling and software solutions that keep the Amazon Music catalog accurate, complete, and trustworthy.

Do you want to join an innovative team of scientists and engineers who use terabytes of data and create state-of-the-art Generative AI algorithms to push the boundaries of AI creativity? We are building foundational behavioral models for Amazon Stores using Generative AI, LLMs and Large Model training techniques that fuses general world knowledge, customer shopping behavior and Amazon e-commerce domain knowledge. We are looking for scientists who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry using intelligent and transformative AI applications. Working closely with cross-functional teams, you will be an essential part of every stage of AI development, from ideation and design to rigorous testing and successful deployment, ensuring our AI projects drive innovation and provide value for our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities In this role you will leverage your background and expertise to lead developing foundational behavioral model for Amazon Stores using Generative AI, LLM and Large Model training techniques. On a day-to-day basis, you will: - Research and implement new algorithms and architectures for generative AI applications. - Optimize model performance and scalability for inference and deployment. - Collaborate with other talented applied scientists and engineers to gather and preprocess large datasets and develop an improved training infrastructure that accelerates innovation. - Experiment with SOTA methods to improve generative AI model quality. - Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services.

Do you want to join an innovative team of scientists and engineers who use terabytes of data and create state-of-the-art Generative AI algorithms to push the boundaries of AI creativity? We are building foundational behavioral models for Amazon Stores using Generative AI, LLMs and Large Model training techniques that fuses general world knowledge, customer shopping behavior and Amazon e-commerce domain knowledge. We are looking for scientists who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry using intelligent and transformative AI applications. Working closely with cross-functional teams, you will be an essential part of every stage of AI development, from ideation and design to rigorous testing and successful deployment, ensuring our AI projects drive innovation and provide value for our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities In this role you will leverage your background and expertise to lead developing foundational behavioral model for Amazon Stores using Generative AI, LLM and Large Model training techniques. On a day-to-day basis, you will: - Research and implement new algorithms and architectures for generative AI applications. - Optimize model performance and scalability for inference and deployment. - Collaborate with other talented applied scientists and engineers to gather and preprocess large datasets and develop an improved training infrastructure that accelerates innovation. - Experiment with SOTA methods to improve generative AI model quality. - Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services.

Employer: Amazon.com Services LLC Position: Applied Scientist II - AMZ27580.1 Location: Nashville, TN Multiple Positions Available: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. (40 hours / week, 8:00am-5:00pm, Salary Range $136000 - $184000) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000

Amazon’s Marketplace business is one of the largest in the world operating in 23 countries. Amazon Marketplace enables millions of Sellers worldwide to list hundreds of millions of products and manage orders for inventory across dozens of different categories and languages. The IN Seller Partner Services Tech organization is responsible for building delightful seller experiences with the vision of being the technology foundation that makes Amazon the #1 choice for sellers of all types and price-sensitive customers in India and emerging markets—transforming how a billion people buy and sell, thereby transforming lives. By making its sellers successful, Amazon can attract and retain sellers on the platform, which in turn offers its customers the best selection, convenience, pricing and experience resulting in multi-billion-dollar business. This organization enables sellers of all stripes across a multitude of business lines existing or new such as Q-Commerce, Pharmacy and Out of country selling. We are looking for a motivated and innovative Applied Scientist with strong analytical skills and practical experience to join our science team. As a key member of our science team, you will provide expertise that helps accelerate the business and make it profitable. You will research, design and improve on the models that will directly impact Amazon’s Selection quality and maximize fee revenue. You will be working in a highly collaborative environment partnering with various science, product management, engineering, operations, finance, business intelligence and analytics teams. You will need to understand the business requirements and translate them into complex analytical outputs. You will design tests to explain performance of the models from impact on customer and cost perspective. You will create ML models to capture features impacting performance. You should be comfortable building prototypes, testing and improving them given the feedback from the real time data. You should be able to present your model and findings to a various range of stakeholders. Looking for candidate with expertise in the areas of machine learning, operations research, and statistics. With expertise in applying theoretical models in an applied environment relying heavily on the latest advances in machine learning, optimization, stochastic modeling, and engineering. The candidate will be expected to work on numerous aspects, such as feature engineering, modeling, probabilistic modeling, hyper-parameter tuning, scalable inference methods and latent variable models. Challenges will involve dealing with very large data sets and requirements on throughput. Key job responsibilities - Design, implement, test, deploy, and maintain innovative science solutions to accelerate our business. - Create experiments and prototype implementations of new learning algorithms and prediction techniques - Collaborate with scientists, engineers, product managers, and stakeholders to design and implement software solutions for science problems - Use best practices to ensure a high standard of quality for all of the team deliverables

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

As an Applied Scientist II specializing in lead scoring and deep learning modeling, you will build and improve machine learning models that power how our business engages with customers. You will develop predictive models for customer segmentation, scoring, and lead/account prioritization, working within an established scoring architecture and collaborating with senior scientists and cross-functional teams to deliver production-grade components. Key job responsibilities * Build and iterate on predictive lead scoring models to support customer acquisition, conversion, and retention strategies using techniques such as survival analysis, graph networks, or transformer-based architectures. * Develop and maintain ML pipeline components for deep learning models, including data preprocessing, feature engineering, model training, and inference integration. * Contribute to internal and external research, including science reviews, technical publications, and patent filings in collaboration with senior scientists. * Apply multi-modal modeling techniques (text, graph, behavioral, and temporal data) to enhance scoring accuracy across account and lead levels. * Conduct A/B testing, causal inference, and counterfactual analysis to measure model impact and iterate on model design. * Partner with MLOps engineers on model deployment, monitoring, and retraining using tools like AWS SageMaker, MLflow, and other internal tools. * Participate in science reviews to maintain and raise the quality bar within the team. * Implement and execute offline and online evaluation frameworks; track success metrics tied to business outcomes (conversion rates, pipeline generation). About the team The AWS Marketing Science team builds the ML models and measurement systems that drive marketing decisions across Amazon Web Services. We own incrementality and valuation, ROI measurement, marketing attribution, propensity scoring, account and lead clustering, and next-best-action models. Our work directly influences how AWS allocates marketing spend, targets accounts, and measures effectiveness across billions in pipeline.