Careers

Shopbop and Zappos are looking for a customer-obsessed Data Scientist to join the Customer Analytics organization. This role will be at the center of how we understand, reach, and serve our customers across every channel, not as a support function, but as a driving force behind our customer strategy. You will build the models that power personalization across sites, email, push, and paid media. You will design the causal frameworks that prove what's actually working versus what just looks like it is. You will apply machine learning, LLMs, and advanced optimization techniques to move us from intuition-driven decisions to evidence-driven ones at scale, across Shopbop and Zappos. The right candidate combines deep technical skills in machine learning and causal inference with genuine curiosity about customer behavior and retail dynamics. They thrive in ambiguity, move fluidly between model development and business strategy, and communicate complex findings clearly to both technical and non-technical audiences. They should have a collaborative mindset that enables them to work effectively across Lifecycle Marketing, Merchandising, Product, Engineering, and other cross-functional partners. This position sits within the Customer Experience organization. Key job responsibilities Design, build, and iterate on customer segmentation models that drive product recommendations, content ranking, intent detection, and customer-specific experiences on site, in email, and in push notifications across Shopbop and Zappos. Apply advanced optimization techniques — including uplift modeling, to improve real-time decisioning across marketing, digital, and channel experiences. Apply causal inference methods grounded in econometric and machine learning frameworks, including EconML, DoWhy, and CausalML, to estimate the true incremental lift of personalization strategies and marketing interventions through techniques such as double machine learning, meta-learners (T-learner, S-learner, X-learner), and targeted maximum likelihood estimation. Build and maintain predictive models for customer preferences and individualized treatment effect models that inform business strategy and investment decisions. Collaborate with Engineering to build scalable data pipelines, feature stores, and real-time serving infrastructure that support ongoing model development and experimentation. Partner with engineering teams to deploy data science models and solutions into production across email, site, and paid media channels, ensuring models translate from development into customer-facing impact. Translate complex analytical and modeling results into clear, actionable recommendations for leadership and cross-functional stakeholders, influencing strategy through evidence rather than intuition.

WW Amazon Stores Finance Science (ASFS) works to leverage science and economics to drive improved financial results, foster data backed decisions, and embed science within Finance. ASFS is focused on developing products that empower controllership, improve business decisions and financial planning by understanding financial drivers, and innovate science capabilities for efficiency and scale. We are looking for a data scientist to lead high visibility initiatives for forecasting Amazon Stores' financials. You will develop new science-based forecasting methodologies and build scalable models to improve financial decision making and planning for senior leadership up to VP and SVP level. You will build new ML and statistical models from the ground up that aim to transform financial planning for Amazon Stores. We prize creative problem solvers with the ability to draw on an expansive methodological toolkit to transform financial decision-making with science. The ideal candidate combines data-science acumen with strong business judgment. You have versatile modeling skills and are comfortable owning and extracting insights from data. You are excited to learn from and alongside seasoned scientists, engineers, and business leaders. You are an excellent communicator and effectively translate technical findings into business action. Key job responsibilities Demonstrating thorough technical knowledge, effective exploratory data analysis, and model building using industry standard ML models Working with technical and non-technical stakeholders across every step of science project life cycle Collaborating with finance, product, data engineering, and software engineering teams to create production implementations for large-scale ML models Innovating by adapting new modeling techniques and procedures Presenting research results to our internal research community

Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Communications Engineer in Modeling and Simulation, this role is primarily responsible for the developing and analyzing high level system resource allocation techniques for links to ensure optimal system and network performance from the capacity, coverage, power consumption, and availability point of view. Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define novel wireless technology with few legacy constraints. The team develops and designs the communication system of Leo and analyzes its overall system level performance, such as overall throughput, latency, system availability, packet loss, etc., as well as compatibility for both connectivity and interference mitigation with other space and terrestrial systems. This role in particular will be responsible for 1) evaluating complex multi-disciplinary trades involving RF bandwidth and network resource allocation to customers, 2) understanding and designing around hardware/software capabilities and constraints to support a dynamic network topology, 3) developing heuristic or solver-based algorithms to continuously improve and efficiently use available resources, 4) demonstrating their viability through detailed modeling and simulation, 5) working with operational teams to ensure they are implemented. This role will be part of a team developing the necessary simulation tools, with particular emphasis on coverage, capacity, latency and availability, considering the yearly growth of the satellite constellation and terrestrial network. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities • Work within a project team and take the responsibility for the Leo's overall communication system design and architecture • Extend existing code/tools and create simulation models representative of the target system, primarily in MATLAB • Design interconnection strategies between fronthaul and backhaul nodes. Analyze link availability, investigate link outages, and optimize algorithms to study and maximize network performance • Use RF and optical link budgets with orbital constellation dynamics to model time-varying system capacity • Conduct trade-off analysis to benefit customer experience and optimization of resources (costs, power, spectrum), including optimization of satellite constellation design and link selection • Work closely with implementation teams to simulate expected system level performance and provide quick feedback on potential improvements • Analyze and minimize potential self-interference or interference with other communication systems • Provide visualizations, document results, and communicate them across multi-disciplinary project teams to make key architectural decisions

Amazon’s Last Mile Delivery organization is responsible for the on-time and error-free delivery of tens of billions of packages annually, in 20+ countries worldwide. The organization’s focus over the years has expanded beyond package delivery to include groceries and heavy and bulky items and has dramatically increased the speed of delivery from two day, to one day, to sub-same day for millions of items, a trend that will continue with quick commerce deliveries within minutes. Underpinning this massive delivery logistics operation (one of the largest in the world) is innovative technology leveraging state of the art AI and ML solutions developed by the Geospatial Science team, one of the largest science teams within the Amazon Operations organization. The Geospatial Science team is responsible for the quality and coverage of the core geospatial data, solvers, and real-time workflows that operate over petabytes of data, power trillions of transit time calculations daily, and operate on diverse environments spanning multi-modal cloud-based learning workflows, highly throughput and low-latency services, and edge compute applications on smart phones, delivery vehicles, and delivery stations. Geospatial Science capabilities operate at the critical path a broad array of mission critical workflows ranging from customer address creation, order placement, delivery route planning, delivery route execution, and package drop-off. The Director, Applied Science (Geospatial) owns the end-to-end science portfolio that enables these capabilities by leveraging innovative AI and ML techniques. They are responsible for (1) learning and improving a worldwide catalog of addresses with high-quality validation and geo-resolution, (2) building a places dataset to model where we delivery ranging from every single single-family home, campus, building, and apartment - along with their relationships and delivery critical attributes such as delivery hours, access information, mail rooms, delivery lockers, parking locations, entrances, and drop-off geocodes, (3) developing maps that capture a fresh and accurate road network, enable precise transit paths that optimize travel times while reducing travel risk in delivery routes and on-road navigation experiences and (4) developing feedback loops that leverage edge capabilities of millions of smart phones and tens of thousands of delivery vehicles to capture fresh street imagery, learn street signs, road markings, and road obstructions at scale, and reconstruct key delivery events and activities to improve the fidelity of address, place, and road datasets, optimize routes, and reduce defects. This leader will lead a worldwide team of approximately 50 scientists, with expertise in generative AI, computer vision, and machine learning. This leader requires broad and deep skills in innovative AI and ML techniques to take advantage of the latest advances in the field. A key focus is accelerating the development and adoption of GenAI-based solutions, in the face of rapid shifts in the science and technology landscape, by guiding the team to maximize the value that can be delivered using latest LLMs, VLMs, agentic paradigms, and reasoning agents. Computer vision based solutions form an important part of the portfolio, as the team innovates on scaled inputs like satellite, aerial, and camera imagery for many problems, such as road learning and transporter safety. This leader will be expected to invest in research and innovation to deliver novel solutions to unlock new opportunities to grow the business while making pragmatic tradeoffs to deliver timely customer value, in conjunction with product, engineering, and operational leaders and teams. This leader will be expected to interface with senior leaders (up to SVP) and senior partners and stakeholders across the World-Wide Operations organization and Amazon. Day-to-day interactions will span product partners with whom s/he will design end-to-end customer solutions and long-term product plans and strategies, engineering partners with whom s/he will execute the development and productization of multi-modal workflows and solvers, and multi-disciplinary upstream and downstream stakeholders and partner teams. This leader will be expected to co-own yearly and 3-year planning documents for the Geospatial Technology space. S/he will also be expected to build and demonstrate advanced research prototypes and proposals up to the SVP level. S/he will be expected to recruit senior scientists and science leaders and managers for their own team as well as other peer teams across Amazon. S/he will need build and maintain a high-performing team and develop and promote scientists and science leaders (up to Principal/Sr Manager/Sr Principal). Key job responsibilities - Lead a worldwide team of scientists to develop and deploy AI and ML solutions for geospatial problems to accelerate and optimize Amazon's global delivery operations - Interface with senior stakeholders across engineering, product, and operations teams to design end-to-end solutions, execute model delivery to production, and drive shared goals - Contribute to strategic planning by developing yearly and 3-year planning documents - Present to senior executives (VPs) and stakeholders via demo sessions, science reports, and quarterly business reports - Drive innovation by leveraging SOTA scientific techniques ranging from GenAI (LLMs/VLMs/agents), computer vision, and traditional ML to solve delivery-related problems - Build organizational capability by recruiting and promoting senior scientists and science leaders and maintain a high-performing team

Do you want to create intelligent, adaptable robots with global impact? The Vulcan Stow team (https://www.amazon.science/latest-news/how-amazon-robotics-researchers-are-solving-a-beautiful-problem) at Amazon Robotics builds high-performance, real-time robotic systems that can perceive, learn, and act intelligently alongside humans—at Amazon scale. We invent and deploy machine learning, optimization algorithms, and geometric reasoning models that empower robots to identify the best available placement opportunities, learn effective stow strategies, and continuously improve capacity utilization. Our mission is to maximize throughput developing intelligent policies that understand physical constraints, reason about geometric action opportunities, and select behaviors that achieve high-density, reliable stowing. We hire and develop ML experts in reinforcement learning, combinatorial optimization, predictive modeling, and decision systems. Our solutions learn from millions of stowing decisions to continuously improve warehouse capacity and throughput. We are seeking an experienced Applied Science Manager for Match and Affordances team to lead a team of talented applied scientists and engineers. You will drive ML innovation using the latest advancements in transformer-based architectures to enable maximum storage utilization, learn affordances and behaviors in high-density environments with the reliability and scale that delights our customers. Collaborating with cross-functional teams across perception, motion planning, and fulfillment operations, you will deliver scalable solutions that optimize stow strategy and warehouse capacity across geographies and conditions. Key job responsibilities People Leadership: Prioritize being a great people manager - motivating, rewarding, and coaching your diverse team is the most important part of this role. Recruit and retain top talent in machine learning, optimization, and decision systems. Excel in day-to-day people and performance management tasks. Technical Vision: Set a vision for your team and create technical roadmaps focused on stow policy development, placement optimization, and density improvements. Guide research, design, deployment, and evaluation of ML and RL algorithms, optimization methods, and geometric reasoning systems that inform robot action selection. Cross-functional Collaboration: Work closely with perception, motion planning, hardware, and fulfillment teams to create integrated solutions that maximize storage density while maintaining operational reliability. Partner with computer vision teams on 3D scene understanding and container geometry representation that drives policy learning. Delivery Excellence: Implement best practices in applied research and software development. Manage project timelines, resources, and deliverables effectively. Keep technical skills current to contribute meaningfully to architecture and design discussions. Problem Solving: Regularly participate in deep-dive troubleshooting exercises and drive technical post-mortem discussions to identify root causes of policy regressions, optimization failures, and KPI degradations. A day in the life - Prioritize being a great people manager: motivating, rewarding, and coaching your diverse team is the most important part of this role. You will recruit and retain top talent and excel in people and performance management tasks. - Set a vision for the team and create the technical roadmap that deliver results for customers while thinking big for future applications. - Guide the research, design, deployment, and evaluation of complex computer vision and machine learning algorithms for contact-rich, cluttered, real-world manipulation problems. - Work closely with motion, hardware, and software teams to create integrated robotic solutions that are better than the sum of their parts. - Implement best practices in applied research and software development, managing project timelines, resources, and deliverables effectively. Amazon offers a full range of benefits for you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan 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! About the team Watch this video to learn more about Vulcan program in Amazon Robotics: https://www.amazon.science/latest-news/how-amazon-robotics-researchers-are-solving-a-beautiful-problem

MULTIPLE POSITIONS AVAILABLE Employer: IMDB.COM, INC. Offered Position: Data Scientist III Job Location: Seattle, Washington Job Number: AMZ9971313 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 $165,006/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

The AWS Neuron Science Team is looking for talented scientists to enhance our software stack, accelerating customer adoption of Trainium and Inferentia accelerators. In this role, you will work directly with external and internal customers to identify key adoption barriers and optimization opportunities. You'll collaborate closely with our engineering teams to implement innovative solutions and engage with academic and research communities to advance state-of-the-art ML systems. As part of a strategic growth area for AWS, you'll work alongside distinguished engineers and scientists in an exciting and impactful environment. We actively work on these areas: - AI for Systems: Developing and applying ML/RL approaches for kernel/code generation and optimization - Machine Learning Compiler: Creating advanced compiler techniques for ML workloads - System Robustness: Building tools for accuracy and reliability validation - Efficient Kernel Development: Designing high-performance kernels optimized for our ML accelerator architectures A day in the life AWS Utility Computing (UC) provides product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Additionally, this role may involve exposure to and experience with Amazon's growing suite of generative AI services and other cloud computing offerings across the AWS portfolio. About the team AWS Neuron is the software of Trainium and Inferentia, the AWS Machine Learning chips. Inferentia delivers best-in-class ML inference performance at the lowest cost in the cloud to our AWS customers. Trainium is designed to deliver the best-in-class ML training performance at the lowest training cost in the cloud, and it’s all being enabled by AWS Neuron. Neuron is a Software that include ML compiler and native integration into popular ML frameworks. Our products are being used at scale with external customers like Anthropic and Databricks as well as internal customers like Alexa, Amazon Bedrocks, Amazon Robotics, Amazon Ads, Amazon Rekognition and many more.

Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced electromechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. Amazon is seeking a talented and motivated Principal Applied Scientist to develop tactile sensors and guide the sensing strategy for our gripper design. The ideal candidate will have extensive experience in sensor development, analysis, testing and integration. This candidate must have the ability to work well both independently and in a multidisciplinary team setting. Key job responsibilities - Author functional requirements, design verification plans and test procedures - Develop design concepts which meet the requirements - Work with engineering team members to implement the concepts in a product design - Support product releases to manufacturing and customer deployments - Work efficiently to support aggressive schedules

At AWS Healthcare AI, we're revolutionizing healthcare delivery through AI solutions that serve millions globally. As a pioneer in healthcare technology, we're building next-generation services that combine Amazon's world-class AI infrastructure with deep healthcare expertise. Our mission is to accelerate our healthcare businesses by delivering intuitive and differentiated technology solutions that solve enduring business challenges. The AWS Healthcare AI organization includes services such as HealthScribe, Comprehend Medical, HealthLake, and more. We're seeking a Senior Applied Scientist to join our team working on our AI driven clinical solutions that are transforming how clinicians interact with patients and document care. Key job responsibilities To be successful in this mission, we are seeking an Applied Scientist to contribute to the research and development of new, highly influencial AI applications that re-imagine experiences for end-customers (e.g., consumers, patients), frontline workers (e.g., customer service agents, clinicians), and back-office staff (e.g., claims processing, medical coding). As a leading subject matter expert in NLU, deep learning, knowledge representation, foundation models, and reinforcement learning, you will collaborate with a team of scientists to invent novel, generative AI-powered experiences. This role involves defining research directions, developing new ML techniques, conducting rigorous experiments, and ensuring research translates to impactful products. You will be a hands-on technical innovator who is passionate about building scalable scientific solutions. You will set the standard for excellence, invent scalable, scientifically sound solutions across teams, define evaluation methods, and lead complex reviews. This role wields significant influence across AWS, Amazon, and the global research community.

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. Our products are used daily to surface new selection and provide customers a wider set of product choices along their shopping journeys. The business is focused on generating value for shoppers as well as advertisers. Our team uses a combination of econometrics, machine learning, and data science to build disruptive products for all our Advertising products. We also generate insights to guide Amazon Advertising strategy, providing direct support to senior leadership. We are looking for an experienced Applied Scientists who have a deep passion for building machine-learning solutions, ability to communicate data insights and scientific vision, and execute strategic projects. As an Applied Scientist on this team, you will: • Build full life-cycle machine learning solutions; build models and perform data analysis to deliver scalable solutions to business problems. • Scale ad performance insights through agentic systems/LLMs. • Perform hands-on analysis and modeling with enormous data sets to develop insights that increase traffic monetization and merchandise sales without compromising shopper experience. • Work closely with software engineers on detailed requirements to productionize the ML models you build. • Run A/B experiments that affect hundreds of millions of customers, evaluate the impact of your optimizations and communicate your results to various business stakeholders. • Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. • Research innovative machine learning approaches. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. BASIC QUALIFICATIONS • PhD or equivalent Master's Degree plus 4+ years of experience in CS, CE, ML or related field • 3+ years of experience of building machine learning models for business application • Experience programming in Python or related language