Careers

Amazon WW Global Selling is looking for a dynamic, highly motivated Sr Data Scientist to join the Selection & Pricing Intelligence team. This is a unique opportunity to: - Play a highly visible role in an exciting and fast-paced business - Drive high-impact frameworks and initiatives that shape which selection Amazon prioritizes globally and how competitively it's priced - Innovate with advanced demand and pricing models, influencing cross-functional and global partners on selection strategy and competitive positioning - Influence and drive decisions of senior leadership, with your models feeding directly into VP-level reviews This role is well-suited for someone with a strong statistics, causal ML, or economics foundation who wants to apply rigorous quantitative thinking to real selection and pricing decisions at scale. You'll need to be comfortable writing SQL, working with imperfect and fragmented cross-marketplace data, and partnering with domain strategists to turn analysis into business action. The ideal candidate will be strong at deriving insight from complex demand and pricing signals, testing whether frameworks generalize across marketplaces and channels, and keen on where AI can scale repeatable analytical judgment. Key job responsibilities - Use advanced statistical and machine learning techniques to extract insights from complex, large-scale, cross-marketplace data sets spanning selection demand and pricing competitiveness - Extend and validate demand models (Unmet Demand Model and related frameworks) to support selection opportunity sizing, including entitlement methodology and compliance-adjusted pool sizing - Build and own pricing measurement frameworks (e.g., price-competitiveness decomposition, competitive positioning metrics) with rigor suitable for senior leadership review - Partner with business/product stakeholders and senior domain strategists to identify strategic, data-driven opportunities across both the selection and pricing intelligence domains - Test whether existing frameworks generalize across marketplaces, channels, or seller cohorts — surfacing where a model breaks down before it drives a flawed business decision - Communicate findings, conclusions, and recommendations to technical and non-technical stakeholders - Design and implement end-to-end data science workflows, from data acquisition and cleaning to model development, testing, and deployment - Support scalable, self-service data analyses by building datasets for analytics, reporting, and ML use cases - Stay current on data science and AI tooling, and help identify which analytical workflows are strong candidates for automation

Every product a customer returns is a moment where Amazon either recovers value or writes it off — and India's ReCommerce business is on a multi-million-dollar mission to recover more of it, more intelligently, at scale. Machine learning is the core lever: predicting whether a returned unit is sellable without a human touching it, detecting damage and fraud inside sealed packaging from images, routing each unit to its highest-value disposition, and pricing recovered inventory dynamically. India's returns network is large, fast-growing, and structurally different from other geographies — a rich, high-impact environment for an Applied Scientist to build models that move real financial and customer-experience metrics. We are hiring an Applied Scientist to build and adapt the ML that powers India ReCommerce. You will work at the intersection of two mandates: building India-first models for problems unique to our market, and adapting proven Worldwide models to India's data, catalog, and operational reality — recalibrating them where distribution, language, and process differ. You will own problems end-to-end, from framing and data through modeling, evaluation, and production deployment, partnering closely with engineering, product, and operations. Key job responsibilities Build ML models for automated returns grading — predicting the salability of returned units from structured and unstructured signals so units can be evaluated with zero or minimal human touch, improving speed, accuracy, and recovery value. Develop computer-vision models for defect detection, condition assessment, and anomaly/fraud identification (including inside sealed packaging), and for establishing chain-of-custody and damage attribution across the returns journey. Build disposition-prediction and routing models that direct each unit to its highest-value recovery path (resale, repair, liquidation, donation, recycle) as early as possible in the network. Develop pricing and recovery-optimization models for liquidation and resale, moving from flat rates toward dynamic, grade- and condition-aware pricing. Adapt Worldwide ML models to India — retraining, recalibrating, and re-evaluating for India's return distribution, catalog, languages, and operational constraints, and closing the gaps that prevent a direct lift-and-shift. Own the full model lifecycle — problem framing, data pipelines, feature engineering, training, offline/online evaluation, monitoring, and retraining — with rigorous attention to calibration, drift, and business-metric impact. Partner cross-functionally with engineering (to productionize), product (to frame problems and measure impact), and operations (to ground models in how the network actually runs), and use modern GenAI/LLM tooling to accelerate research and delivery. A day in the life You start by reviewing the performance of a grading model in production — checking calibration and drift against last week's returns, and confirming the recovery-value lift is holding. Mid-morning, you dig into a computer-vision problem: improving detection of a damage type that's driving write-offs, using images captured across the returns journey. In the afternoon you work with a Worldwide science team to bring one of their models to India — scoping what retraining and recalibration India's data requires — then pair with an engineer to move your latest model toward production behind a clean evaluation gate. You close by framing a new problem with a product partner: quantifying the opportunity, defining the label and success metric, and sketching the modeling approach. About the team India ReCommerce owns the systems and science that turn returned and unsellable inventory into recovered value and a better customer experience. You will join a team building an increasingly automated, ML-driven returns network — leveraging Worldwide platforms where they fit and building India-first capabilities where they don't. It is a high-ownership environment with a direct line from your models to measurable business and customer outcomes.

Have you wondered at the speed at which your Amazon purchase arrived at your door, in that box with a smile? wondered where it came from and how much it cost Amazon to deliver it to you? Amazon Last Mile Strategic Planning is looking for Sr. Data Scientists, developing solutions to optimize our delivery network topology, strategically maximizing Customer Experience and minimizing cost to serve and increase speed. You will partner with the Scientific community to help design optimization and strategy. You will also collaborate with technical teams developing automated tools for network flow and execution systems. You will work directly with business leadership and operational stakeholders to influence their strategy and gather inputs to solve problems. To be successful in the role, you will need deep analytical skills and a strong scientific background. The role also requires excellent communication skills, translating technical contents to business friendly narrative. Ability to influence across business functions at different levels, including your own team. You will work in a fast-paced environment that requires you to be detail-oriented and comfortable in working with data, science, business and technical teams. Key job responsibilities -Design and develop mathematical, statistical and optimization models to optimize Delivery Network Topology design.. -Manage several, high impact projects simultaneously -Consult and collaborate with business and technical stakeholders across multiple teams to define new opportunities to optimize Delivery Network Topology -Communicate data-driven insights and recommendations to diverse stakeholders through technical and/or business papers -Leverage LLMs to improve explainability of optimization and drive engagement from volume planning, demand planning stakeholders -Define measurement frameworks for optimization solutions where no prior art exists and own the scientific framework for ‘Topology Plans’ multi-contact journey -Choose the right methods (statistical, causal, ML, LLM, hybrid) for each problem and justify trade-offs. Drive excellence in evaluation: ground-truth construction with Quality auditors, human audits, precision/recall, drift, calibration, bias, safety, and cost - Design driver-analysis and bridging methods explaining KPI movement (WoW, MoM, YoY, vs OP2) across dimensions for "why" - Partner with teams in productionizing; Own AWS tech stack compliancy (Shepherd risk, App Security red-certification, Kale, Legal, Threat Models, for scientific assets) - Mentor team members; provide promotion assessments; contribute hiring at DS II and DS III. Represent LM Planning in the broader Amazon Data Science community - Produce design and technical documentation A day in the life Review current solutions, assumptions and question the status quo to find improvements. Drive technical partners adopt improvements, to increase coding velocity, accuracy, quality of scientific solutions. Research for reusable tools/techniques and translate adopting to About the team Last Mile is the final mile of Amazon purchase. We- LM Strategic Planning ‘design and plan the Delivery Station Network’. LM network continues to grow multi-fold in North America, AMET, Emerging Market countries, delivering better customer experience and speed to customers. This growth will help Amazon gain most of distribution network share, in every country across the world. LM Strategic Planning-Science Analytics & Automation provides foundational solutions for network expansion roadmap that looks a 1-5 years horizon.

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best.

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the limits. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. As a Senior Applied Scientist on our team, you will focus on building state-of-the-art ML models for healthcare. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams. This role offers a unique opportunity to work on projects that could fundamentally transform healthcare outcomes. Key job responsibilities In this role, you will: • Design and implement novel AI/ML solutions for complex healthcare challenges • Drive advancements in machine learning and data science • Balance theoretical knowledge with practical implementation • Work closely with customers and partners to understand their requirements • Navigate ambiguity and create clarity in early-stage product development • Collaborate with cross-functional teams while fostering innovation in a collaborative work environment to deliver impactful solutions • Establish best practices for ML experimentation, evaluation, development and deployment • Partner with leadership to define roadmap and strategic initiatives You’ll need a strong background in AI/ML, proven leadership skills, and the ability to translate complex concepts into actionable plans. You’ll also need to effectively translate research findings into practical solutions. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the Special Projects organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. About the team We represent Amazon's ambitious vision to solve the world's most pressing challenges. We are exploring new approaches to enhance research practices in the healthcare space, leveraging Amazon's scale and technological expertise. We operate with the agility of a startup while backed by Amazon's resources and operational excellence. We're looking for builders who are excited about working on ambitious, undefined problems and are comfortable with ambiguity.

Robots are about to step out of the movies and into our homes. If you're excited about this robotics revolution, come join Amazon's Fauna Robotics. As an Applied Scientist in this org, you'll push the state-of-the-art techniques in Robotics. You'll drive technical excellence in areas such as perception, manipulation, sim2real transfer, reinforcement learning and multi-task learning, designing novel algorithms that bridge the gap between research and real-world deployment. In this role, you will integrate hands-on technical expertise with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments. While staying updated with the latest advancements in robotics research, you will leverage your experience and expertise to guide the team towards the most promising direction. Collaborating with a talented group of software engineers and scientists, you will bring your ideas to fruition. Deploying robots in home environments requires you to anticipate challenges not encountered in structured settings like warehouses. Additionally, consumer-grade sensors and actuators necessitate working around their limitations, requiring out-of-the-box innovative ideas. As such, you are someone who truly enjoys thinking about problems using first principles at a holistic and systems level to address the aforementioned challenges. Key job responsibilities - Lead technical initiatives in areas such as robotics foundation models, reinforcement learning and manipulation - - Design experiments to identify the limitations of current state-of-the-art models and developing new models or techniques that can surpass these models - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Mentor fellow scientists while maintaining strong individual technical contributions - Collaborate with engineering teams to optimize and scale models for real-world applications - Influence technical decisions and implementation strategies within your area of focus - Experienced in communicating complex technical work to a non-technical audience - The ability to work with minimal guidance, be proactive and to handle ambiguity and the challenge of quickly evolving goals A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Guide fellow scientists in solving complex technical challenges, from sim2real transfer to training RL policies - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.

As a Principal Applied Scientist for Full-Funnel Campaign optimization, you will invent the models that jointly allocate budget across sponsored ad products to maximize advertiser outcomes and long-term customer value. This is a rare charter to build foundational optimization science where little exists today, spanning campaign recommendation, cross-product budget allocation, incrementality measurement, and long-term-sales modeling. You will set technical direction for a growing team, partner with engineering and product to take models from research to production at Amazon scale, and directly move advertiser ROAS and new-to-brand growth. - Own the science vision for full-funnel campaign optimization end to end - Invent models for joint budget allocation, incrementality, and long-term value - Take innovations from prototype to production serving live advertiser campaigns - Raise the technical bar across scientists and engineers Key job responsibilities - Define the long-term scientific vision for full-funnel campaign optimization, translating ambiguous advertiser needs and competing objectives into a concrete science roadmap. - Invent, prototype, and productionize machine learning and optimization solutions for joint budget allocation across sponsored ad products, spanning the shopper journey from awareness to purchase. - Develop rigorous approaches to incrementality measurement and long-term-sales modeling that ground optimization in true advertiser value. - Design and lead large-scale experiments and analyses to validate hypotheses and guide product direction. - Partner closely with engineering and product to define technical contracts, data schemas, and serving systems that carry models into production. - Raise the technical bar across science and engineering through mentorship, design reviews, and hands-on collaboration. - Grow scientific talent and publish impactful research internally and at top-tier venues. A day in the life You move between deep technical work and org-wide influence. A morning might be spent deriving a budget-allocation formulation with two scientists, then reviewing an incrementality experiment design over an advertiser segment. Afternoons bring roadmap alignment with product and engineering partners, a design review that raises the bar on a teammate's model, and a working session on taking a prototype to production. Your customers are Amazon advertisers and their shoppers; your stakeholders span applied science, engineering, and product leadership across the Ads full-funnel organization. About the team We build the optimization science behind full-funnel advertising on Amazon: how campaigns are recommended, and how budget is allocated across sponsored ad products to grow advertiser outcomes and long-term customer value. Our mission is to make full-funnel advertising work automatically and measurably for every advertiser, from foundational research through production systems serving live campaigns. We are a science-driven, high-ownership team that values rigorous experimentation, invention where no proven approach exists yet, and close partnership with engineering and product.

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Develop AI solutions for various Prime Video Search systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field; - Publish your research findings in top conferences and journals. About the team Our team works at the intersection of generative recommendations, multi-objective reinforcement learning, and whole-page optimization. We're rethinking how recommendation systems construct experiences end-to-end, moving beyond ranked lists toward intelligent, adaptive page-level decision-making at scale.

Worldwide Global Selling has been helping individuals and businesses increase sales and reach new customers around the globe. Today, more than 50% of Amazon's total unit sales come from third-party selection. The Global Selling team in China is responsible for recruiting local businesses to sell on Amazon's 19+ overseas marketplaces and supporting local Sellers' success and growth on Amazon. Our vision is to be the first choice for all types of Chinese business to go globally. The Worldwide Global Selling Analytics, Intelligence, and Technology (WWGS-AIT) team serves as the research, automation, and insight arm of the International Seller Service data hub, enabling rapid delivery of growth insights through strategic investments in regional data foundations, self-service business intelligence solutions, and artificial intelligence tools. The WWGS-AIT team is positioned to establish AI-ready foundational capabilities across the WWGS organization while maintaining excellence in business insight generation, and self-service BI/AI application development. WWGS-AIT is looking for a Senior Data Scientist to build reusable science capabilities that support seller growth, operational decision-making, and cross-domain innovation across Worldwide Global Selling. You will lead high-impact modeling initiatives at the intersection of graph science, machine learning, simulation, and optimization. Your initial focus will include building identity-resolution and entity-linkage capabilities that create a trusted One ID view across fragmented seller and business data; developing simulation and decision models for logistics and inventory options; and establishing reusable modeling foundations for seller lifecycle and other cross-domain use cases. You will also partner with business, product, engineering, and analytics teams to evaluate and deliver prioritized science opportunities through a common intake process. This role is ideal for a hands-on scientist who can move from ambiguous business problems to robust, production-ready models and decision systems. Key job responsibilities - Lead the design, development, and productionization of graph-based identity-resolution and entity-linkage models that connect seller, account, business, logistics, and other relevant entities into a trusted One ID foundation. - Develop simulation, optimization, forecasting, and decision-support models for logistics, inventory, and related operational choices; quantify trade-offs, uncertainty, and expected business impact. - Establish scalable model-development practices, including feature engineering, experiment design, model validation, monitoring, reproducibility, documentation, and responsible-use controls. - Translate ambiguous business questions into clear science problems, measurable hypotheses, model requirements, and decision frameworks. - Partner with Data Engineering, BIE, Product, and domain teams to build reliable data pipelines, model features, evaluation datasets, and production model interfaces. - Support prioritized science needs from Supply Chain, Seller Success, and other teams through the WWGS-AIT operating-planning intake and prioritization process. - Define model performance, business-impact, and operational-success metrics; use offline evaluation, back-testing, simulation, and controlled experiments to continuously improve solutions. - Contribute applied AI and GenAI expertise where it improves science-enabled products—for example, model evaluation, retrieval/ranking, intelligent decision support, or AI-agent capabilities grounded in trusted data and models. - Influence the WWGS science roadmap by identifying opportunities to convert repeated business problems into durable, reusable data and modeling capabilities.

We are seeking an Applied Science Manager to lead a new business unit on the GameLift team focused on creating AI and ML-based applications for the gaming industry. This leader will own the technical vision, scientific rigor, and end-to-end delivery of applied science initiatives that solve complex problems in machine learning, data science, and live-service gaming at scale. The ideal candidate operates at the frontier of AI research and deployment, translating ambiguous business opportunities into production-grade ML systems that generate measurable customer and commercial impact. This role demands a hands-on technical leader who can define and execute an applied science roadmap while managing and mentoring a high-performing team of builders, scientists and ML engineers. You will be responsible for upholding the highest standards of scientific excellence, including rigorous experimental design, disciplined model selection, and reproducible evaluation methodology, while maintaining the speed and inventive culture of a startup operating within a large organization. You will partner with engineering, product, and business stakeholders to bring AI-powered products from research through production deployment, meeting customer requirements and delivery timelines. The successful candidate will be equally comfortable debating the merits of deep learning architectures in a technical review as they are presenting a product roadmap to senior leadership, and will thrive in an environment where building something new from zero to one is the daily expectation. Key job responsibilities Define and execute the technical roadmap for applied science initiatives, balancing frontier research with production delivery requirements and customer timelines Lead rigorous model development processes including algorithm selection, offline evaluation, A/B testing design, and statistical significance assessment, ensuring every production decision is grounded in scientific evidence rather than intuition Architect scalable, reusable ML platforms and inference systems designed to serve multiple products without proportional increases in staffing or rebuild cycles, enabling the team to move fast across a growing portfolio Manage and develop a team of applied scientists and ML engineers, providing technical mentorship, career growth opportunities, and performance management while maintaining a high hiring bar Drive end-to-end AI deployment from research prototyping through production inference, owning latency, availability, and cost targets alongside model quality metrics Establish and enforce scientific standards across all team projects, including peer review mechanisms, documentation requirements, and reproducibility practices that ensure technical decisions withstand scrutiny Partner cross-functionally with product managers, software engineers, and business leaders to translate customer problems into well-scoped technical solutions with clear success criteria Maintain a builder roadmap that sequences product launches against customer commitments, managing dependencies and communicating tradeoffs to stakeholders when scope or timeline pressure arises Enable the team to operate with startup-level autonomy and speed of invention while maintaining the operational discipline required for production systems serving customers at scale Stay current with developments across the AI/ML research landscape, identifying opportunities to apply new techniques A day in the life Your morning starts with production system health checks: inference latency, model freshness, experiment dashboards. Mid-morning you lead a technical design review, challenging your scientists on model complexity tradeoffs and coaching toward disciplined, phased approaches that maintain rigor without sacrificing speed. After lunch you join a cross-functional sync with product and engineering to align on delivery milestones, working through scope tradeoffs when customer requirements shift. Late afternoon is for people leadership: one-on-ones focused on career growth, reviewing hiring scorecards to keep the bar high, and scanning recent research for techniques your team can apply next quarter. Every day blends science, product delivery, and team building.