Careers

Are you energized by building technology that makes life easier for business customers? If you enjoy working at the center of enterprise commerce, connecting Amazon Business with the procurement, ERP, and finance systems that organizations run on ethn Amazon Business System (ABS) Integrations team is the place for you. We build systems integrations capabilities that let business customers buy on Amazon Business directly from the systems they already use. This includes APIs, protocols, and partner connections that make procurement faster, more automated, and more reliable at global scale. Our teams design, launch, and operates these integrations with a wide range of partners, and each launch has to work securely and seamlessly across many different systems, regions, and customer needs. We are seeking a Senior Manager Applied Science to build and lead the science organization in Amazon Business Systems Integrations. This is a foundational leadership role spanning the full integrations portfolio. This role will hire, grow, and lead a team of applied scientists who define how we measure and improve the performance of AI agents and human-AI teams. A core part of the role is defining the science agenda itself - identifying which problems are most worth solving and where the highest-leverage bets lie. Directions worth exploring might include Organizational Intelligence (turning institutional knowledge into agent-consumable skills) AI Agent Experience / AiAX (agent observability and autonomous remediation) and contextual behavioral security that adapts enforcement in real time for human and agent sessions and anything which the defines how we measure and improve agents and human-AI teams i.e benchmarks, task suites, and metrics (accuracy cost-per-task task completion productivity) that turn subjective "it works" judgments into rigorous reproducible measurement that gates what we ship. There are no established industry patterns in this field and the team will set the scientific direction, determine how AI agents and people perceive reason and act reliably within computing environments at enterprise scale. This role requires building, leading and retaining a high calibre applied science team spanning the Agentic Integrations portfolio. This leader will set the bar for scientific talent, create the growth paths, and build the culture that makes Amazon Business a destination for the best agent and human-AI researchers. Key job responsibilities • Set the long-term scientific vision and team strategy: Define what best-in-class agent performance evaluation and learning look like across Agentic WorkSpaces - for computer-using agents and human-AI teams alike. Chart a multi-year research roadmap and build the team and plan to deliver it. Secure buy-in from VP-level leadership. • Hire and grow scientific talent: Own recruiting calibration development and retention for the science team. Mentor scientists toward senior and principal scope and raise the scientific bar across the organization. • Direct research on highly ambiguous novel problems: Guide the team through foundational challenges in agent perception reasoning evaluation reliability and human-AI collaboration - problems where neither the approach nor the success criteria are pre-defined. • Drive cross-organizational alignment: Work across partner teams (AgentCore Bedrock model teams Identity Security the MCP ecosystem) and across the Applied AI Solutions product portfolio with product and engineering leadership to ensure scientific decisions compose into a coherent product. • Deliver measurable business impact: Ensure your team's research translates to customer outcomes: higher task accuracy lower cost-per-action faster time-to-production measurable productivity for human-AI teams and the trust that lets enterprises scale agent workflows. • Establish scientific rigor and operational excellence: Set the standard for experimentation evaluation and reproducibility and the mechanisms that keep the science organization productive and accountable. • Advance the state of the art: Enable and champion contributions to the external technical community through publications patents and open-source work that position AWS as the leader in the science of secure agent-computer interaction and human-AI teamwork. • Translate science into shipped product. Partner with engineering, product, and program leaders to move models evaluation and learning systems from prototype into a decade-old production service operating at massive scale without compromising the reliability that customers depend on. • Represent science in leadership and to customers. Be the scientific voice in org-level planning and roadmap decisions across AAWS and engage directly with enterprise customers on how agent performance safety and human-AI productivity are measured and earned.

At Amazon, we are obsessed with customer trust. Amazon Security enables Amazon's business leaders to build and operate securely while protecting the confidentiality and integrity of Amazon and customer data. We assess risk, classify data and systems, detect intrusion, and drive mitigation at scale. Veritas is Amazon's single source of truth for applications and services and the resources that make them up, spanning both Stores/Devices (SDO) and AWS. Security teams across Amazon rely on Veritas to understand attack paths, assign issue ownership, track the flow of sensitive data, and reason about risk across one of the largest and most diverse technology footprints in the world. We are seeking a talented, self-directed Senior Data Scientist to work on cutting-edge security technologies. You will take the lead on large, ambiguous problems that have not been solved before - defining both the problem and the solution approach autonomously. You will design and run experiments, research new algorithms, and find new ways to protect Amazon's customer trust. You will work closely with talented software engineers and security engineers who transform your prototypes and models into production. You should thrive in ambiguous environments and enjoy fast-paced settings where learning new concepts quickly is essential. You leverage exceptional technical expertise, strong statistical skills, and data acumen to analyze large volumes of business data and make Amazon safer. Your strong communication skills enable you to influence across multiple teams and to work effectively with both business and technical partners, including senior leadership. Key job responsibilities - Define problems and solution approaches autonomously in ambiguous, open-ended security problem spaces where no established pattern exists. - Process and analyze large data sets using the full range of techniques required, selecting the ideal method from a wide range of data science disciplines. - Deliver scalable models that analyze large data sets efficiently. - Build mathematical models to detect and classify specific data elements with high accuracy. - Prototype models in high-level modeling languages such as R, or in software languages such as Python. A software team will partner with you to transform prototypes into production. - Take the lead on large projects that require both business and technical domain expertise, delivering significant benefit to the business. - Influence multiple teams and build consensus across business, science, and engineering stakeholders; advise senior managers and directors on data-driven security decisions. - Mentor and develop other scientists; perform data science technical assessments and participate in science and data-engineering code reviews. - Create, enhance, and maintain technical documentation, and present findings to other scientists and business leaders, keeping teams current on relevant scientific advancements.

The Prime Video Science team leverages the latest in machine learning and AI techniques combined with causal inference to bring scientific rigor to the biggest decisions in entertainment: what content to make, what to license, and where to invest. We build large-scale models that simulate how our global customer base responds to change, and we get to see that work shape what the business does. Prime Video is an industry-leading entertainment business and a critical driver of Amazon Prime subscriptions, contributing to customer loyalty and lifetime value. We're looking for an Economist to help us design the experiments that ground our models in reality, evaluate the AI systems we build, and turn our model and experiment results into insights the business can act on. As an Economist on this team, you will design and analyze experiments, build evaluations for AI systems, and run deep-dive analyses on our models, experiments, and customer data. You will work close to science: probing why a model behaves the way it does, pressure-testing results before they reach senior leaders, and, where it helps, building your own statistical and causal models. The candidate should have strong communication skills and the ability to translate complex, ambiguous analyses into clear findings for both technical and business audiences. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and the ability to work in a fast-paced and ever-changing environment. Key job responsibilities • Design and analyze randomized experiments that validate and calibrate our models and measure the impact of content and product changes. • Build evaluations and benchmarks for the AI systems the team develops and define what "good" looks like for them. • Run deep-dive analyses on model outputs, experiment results, and customer behavior to surface the story behind the numbers and catch issues before they reach stakeholders. • Apply statistical modeling, causal inference, and data analysis to answer business questions and inform major investment decisions. • Communicate findings to business, finance, engineering, and science stakeholders through clear written analyses and business-facing documents. About the team The Prime Video Science team is a multidisciplinary group of applied scientists, data scientists, economists, and engineers. We take on some of the hardest research questions in the business, and our work carries visibility up to the CFO/CEO level. We pursue ambitious research at the intersection of machine learning, AI, and causal inference, turning that research into innovations that improve customer experience and strengthen business profitability. Few science teams get to work on problems this hard and this impactful; if that combination excites you, we'd love to talk.

We're building systems that turn research into real-world impact — developing AI-driven detection systems that identify and remediate security access violations across Amazon's global operations, and building ML solutions that leverage product images, text, and tabular data to improve safety and compliance mechanisms across Amazon's entire product catalog. If this sounds interesting to you then we have a range of opportunities for you to explore. We're looking for Masters students across multiple research domains to invent, design, and implement state-of-the-art solutions for never-before-solved problems. Your work here won't just stay in a notebook — it ships to production and reaches customers worldwide. Check out the details below including the job responsibilities, team details, and basic qualifications before submitting your application. You can find more information about the Amazon Science community as well as interview preparation tips via the links below; - https://www.amazon.science/ - https://amazon.jobs/content/en/career-programs/university/science - https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You'll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon's customer focused culture. The ideal intern should have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life You'll spend your first weeks scoping your project with your mentor, then own the research and implementation end-to-end. Your work could involve building graph-based ML models and knowledge graphs that detect and remediate access violations at global scale, developing behavioral pattern clustering and automated reasoning systems for security operations, or researching multimodal and computer vision approaches on product catalog data to improve safety and compliance mechanisms — depending on the team you're matched with. Many interns publish at top-tier conferences or see their work deployed to production before the internship ends. Interns may also be considered for a return offer at the end of their internship, subject to performance evaluation and headcount availability. Further benefits of an Amazon Science internship include; - All of our internships offer a competitive salary - Interns are paired with an experienced manager and mentor(s) - Interns get invited to different intern program or office events - Interns can build their professional and personal network with other Amazon Scientists - Interns can potentially publish work at top tier conferences About the team We generally hire interns for multiple teams in Romania, including but not limited to; • CoSS — Research and develop ML solutions that improve safety and compliance across Amazon's entire product catalog working with tabular, textual, and product image data using deep learning, computer vision, LLMs, and multimodal models • Monitored Access Detection — Build AI-driven detection and remediation systems that identify global access violations across Amazon — leveraging knowledge graphs, graph modeling, behavioral pattern clustering, generative AI, and reinforcement learning to create a self-improving, closed-loop security system. You'll submit a single application and we'll match you with science teams best aligned with your research interests. Applications are reviewed on a rolling basis, and your application stays active until we find a team match or confirm there are no matches available. Start dates are available throughout the year for durations of between 3–6 months. Please note, each team has different start date and duration preferences — your recruiter will confirm the preferences of the team you're matched with prior to interviewing. We offer science internships in multiple locations across the EMEA region and you can indicate your interest in all these locations by applying here (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, South Africa, Spain, Sweden, UAE, and UK). Please note we do not offer remote internships.

We are seeking an Senior Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers 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.

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Selling Partner Trust & Store Integrity Science Team. We are looking for a talented scientist who is passionate to build advanced machine learning systems that help manage the safety of millions of transactions every day and scale up our operation with automation. Key job responsibilities - Innovate with the latest GenAI/LLM/VLM technology to build highly automated solutions for efficient risk evaluation and automated operations - Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to create impactful business value - Learn, explore and experiment with the latest machine learning advancements to create the best customer experience A day in the life You will be working within a dynamic, diverse, and supportive group of scientists who share your passion for innovation and excellence. You'll be working closely with business partners and engineering teams to create end-to-end scalable machine learning solutions that address real-world problems. You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation. You will also be providing clear and compelling reports for your solutions and contributing to the ongoing innovation and knowledge-sharing that are central to the team's success.

Join our Amazon Private Brands Selection Guidance organization in building science and tech solutions at scale to delight our customers with products across our leading private brands such as Amazon Basics, Amazon Essentials, and by Amazon. The Selection Guidance team applies Generative AI, Machine Learning, Statistics, and Economics solutions to drive our private brands product assortment, strategic business decisions, and product inputs such as title, price, merchandising and ordering. We are an interdisciplinary team of Scientists, Economists, Engineers, and Product Managers incubating and building day one solutions using novel technology, to solve some of the toughest business problems at Amazon. As a Data Scientist you will investigate business problems using data, invent novel solutions and prototypes, and directly contribute to bringing your ideas to life through production implementation. Current research areas include named entity recognition, product substitutes, pricing optimization, agentic AI, and large language models. You will review and guide scientists across the team on their designs and implementations, and raise the team bar for science research and prototypes. This is a unique, high visibility opportunity for someone who wants to develop ambitious science solutions and have direct business and customer impact. Key job responsibilities - Partner with business stakeholders to deeply understand APB business problems and frame ambiguous business problems as science problems and solutions. - Perform data analysis and build data pipelines to drive business decisions. - Invent novel science solutions, develop prototypes, and deploy production software to solve business problems. - Review and guide science solutions across the team. - Publish and socialize your and the team's research across Amazon and external avenues as appropriate - Leverage industry best practices to establish repeatable applied science practices, principles & processes.

We are looking for an engineer who builds data pipelines and writes code to understand a live satellite network. 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. You will monitor Amazon Leo's live satellite network against what our simulations predict, and use what you find to improve the performance our customers receive. You will help build and run an automated pipeline that compares real-time system and network data with simulation predictions, finds where performance falls short, and helps drive each problem to resolution with the many teams involved. A problem can sit anywhere in the system: satellites and their optical inter-satellite links, gateways, user terminals, the satellite telemetry, tracking and command (TT&C) link, and the software that connects them. The team also supports satellite operations: launch and early orbit, gateway and TT&C station assignments, and the satellite communication links that carry them. The team takes part in an on-call rotation for problems that affect customers or operations. You will join a new, interdisciplinary team that covers software and data pipelines, radio frequency (RF) and satellite communication link performance (user, gateway and TT&C links), software and data pipelines, artificial intelligence and machine learning (AI/ML) automation, networking, systems integration and test, and production operations and troubleshooting, and that works closely with the modeling and simulation, hardware, software and operations teams across Amazon Leo. Export Control Requirement Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident that is a current Green Card holder, or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities Data pipeline and performance framework - Build, maintain and use the team's automated pipeline that brings live system and network data, simulation predictions and test measurements together - Design how the data is stored, queried and kept at scale, so that the pipeline runs reliably and unattended across the whole network - Write the code that joins sources logged at different rates and times, and keep each record's context with it so that later analysis can explain it - Integrate simulations and models developed by other teams into the team's framework, and co-develop each tool's automation interface with the team that owns it - Compare live performance with simulation predictions within tolerances that reflect what each prediction means, and report when the model, not the network, looks wrong Rapid detection of changes that affect customers - Build AI, machine learning and automation steps into the pipeline to rapidly detect, group and triage changes that affect customers, and check their conclusions against independent evidence - Develop statistical change-detection methods that flag when performance moves away from the network's own history - Turn large telemetry data sets into trends, visualizations and findings the team and its partners can act on Troubleshooting and resolution - Help trace performance problems to their root causes across RF, software, hardware and networking, by comparing affected and unaffected terminals, satellites and gateways - Confirm a cause with an independent measurement, or reproduce the fault in simulation or test - Hand each cause, with its evidence, to the team that owns the fix, and verify after the fix that customers see the improvement Documentation and collaboration - Build up a database of anomaly signatures, so that each investigation builds on earlier ones and known problems are recognized quickly - Work with the simulation, test, satellite, gateway, terminal and software teams across our organization to get the data and evidence each investigation needs A day in the life About the team We are a new team inside Amazon Leo’s communication systems organization. We turn live network data into clear answers about the performance customers receive, and then work with subject matter experts to improve the customers’ experience. Amazon Leo’s network serves consumers, businesses and government organizations; within Amazon Leo, the team supports the engineering and operations teams that need reliable evidence of how the network performs.

We are looking for a manager to build and lead a team that monitors Amazon Leo's live satellite network against what our simulations predict, and uses what it finds to improve the performance our customers receive. 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. The team develops an automated pipeline that compares real-time system and network data with simulation predictions, finds where performance falls short, and drives each problem to resolution with the many teams involved. A problem can sit anywhere in the system: satellites and their optical inter-satellite links, gateways, user terminals, the satellite telemetry, tracking and command (TT&C) link, and the software that connects them. The team also supports satellite operations: launch and early orbit, gateway and TT&C station assignments, and the satellite communication links that carry them. The team takes part in an on-call rotation for problems that affect customers or operations. This is a critical, new role. You will hire and build an interdisciplinary team that covers radio frequency (RF) and satellite communication link performance (user, gateway and TT&C links), software and data pipelines, artificial intelligence and machine learning (AI/ML) automation, networking, systems integration and test, and production operations and troubleshooting, and that works closely with the modeling and simulation, hardware, software and operations teams across Amazon Leo. Export Control Requirement Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident that is a current Green Card holder, or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities The team starts small. Surrounding teams will support this work while you hire and build the team that owns it. Team leadership • Hire and grow a team that covers each discipline the work needs: RF and link performance, software and data pipelines, AI/ML automation, networking, systems integration and test, and production operations and troubleshooting • Prioritize the team's work with engineering and operations partners, so that effort goes where it most improves the customer experience • Partner with the simulation, test, satellite, gateway, terminal and software teams on the data and evidence the analysis needs Performance prediction and detection framework • Integrate simulations and models developed by other teams into a comprehensive framework that predicts network performance and detects where live performance falls short • Own the team's automated pipeline that brings live system and network data, simulation predictions and test measurements together • Co-develop each tool's automation interface with the team that develops and calibrates it • Set tolerances that reflect what each prediction means, and recognize when the model, not the network, is wrong Rapid detection of changes that affect customers • Employ AI, machine learning and automation tools to rapidly detect changes that affect users, so the team catches problems early and protects the customer experience • Detect changes against the network's own history with statistical change-detection methods • Require independent evidence before an automated finding is treated as confirmed End-to-end troubleshooting and resolution • Drive performance problems to their root causes across RF, software, hardware and networking, wherever they sit in the end-to-end system • Compare affected and unaffected terminals, satellites and gateways, and confirm causes with independent measurement or reproduction • Hand each cause, with its evidence, to the team that owns the fix, and confirm after the fix that customers see the improvement Customer impact and reporting • Show how each finding affects the end-to-end customer experience • Report findings, open questions and progress to leadership on a regular cadence About the team We are a new team inside Amazon Leo’s communication systems organization. We turn live network data into clear answers about the performance customers receive, and then work with subject matter experts to improve the customers’ experience. Amazon Leo’s network serves consumers, businesses and government organizations; within Amazon Leo, the team supports the engineering and operations teams that need reliable evidence of how the network performs.

Are you inspired by invention? Do you want to see how your work impacts the bigger picture? If yes, you'll fit right in at Amazon's Last Mile Simulations and Analytics Engineering team. The WW AMZL Simulations and Analytics Engineering team drives innovation across our Last Mile network delivery stations through digital twin technology and the seamless integration of process engineering, physics, design, technology, and robotics. We start with the customer and work backwards in everything we do. If you're interested in joining a rapidly growing team building a unique, solutions-advisory group with a relentless focus on the customer, you've come to the right place. This is a blue-sky role where you'll roll up your sleeves and dive into big data sets to build digital twins—discrete event simulations, robotics models, and physics-based 3D simulations using tools like Flexsim, AnyLogic, and Emulate 3D. You'll develop experimentation systems at scale, integrating process workflows, physical dynamics, facility design, emerging technologies, and robotic automation to create comprehensive virtual replicas of our operations. Build optimization algorithms and leverage advanced technologies across Amazon. This is an opportunity to think big about solving challenging problems for our customers. As a Senior Simulation Scientist, you'll deep dive into complex problems and drive relentlessly towards innovative solutions with cross-functional teams. You'll interface and influence various functional teams and individuals at all levels of the organization. Lead strategic digital twin and simulation projects that integrate process design, physics modeling, technology innovation, and robotics to drive operational decisions. Your expertise in synthesizing and communicating insights to audiences of varying technical sophistication will enable you to answer specific business questions and innovate for the future. You'll apply advanced multi-disciplinary methodologies—combining process optimization, physics simulation, design thinking, and robotic systems—to complex use cases across the Last Mile network. You'll also contribute to the end-state vision for digital twin simulation and experimentation of future delivery stations at Amazon. Key job responsibilities • Lead the design, implementation, and delivery of the simulation data science solutions to perform system of systems discrete event simulations for significantly complex operational processes that have a long-term impact on a product, business, or function using FlexSim, Demo 3D, AnyLogic or any other Discrete Event Simulation (DES) software packages • Lead strategic modeling and simulation research projects to drive process design decisions • Be an exemplary practitioner in simulation science discipline to establish best practices and simplify problems to develop discrete event simulations faster with higher standards • Identify and tackle intrinsically hard process flow simulation problems (e.g., highly complex, ambiguous, undefined, with less existing structure, or having significant business risk or potential for significant impact • Deliver artifacts that set the standard in the organization for excellence, from process flow control algorithm design to validation to implementations to technical documents using simulations • Be a pragmatic problem solver by applying judgment and simulation experience to balance cross-organization trade-offs between competing interests and effectively influence, negotiate, and communicate with internal and external business partners, contractors and vendors for multiple simulation projects • Provide simulation data and measurements that influence the business strategy of an organization. Write effective white papers and artifacts while documenting your approach, simulation outcomes, recommendations, and arguments • Lead and actively participate in reviews of simulation research science solutions. You bring clarity to complexity, probe assumptions, illuminate pitfalls, and foster shared understanding within simulation data science discipline • Pay a significant role in the career development of others, actively mentoring and educating the larger simulation data science community on trends, technologies, and best practices • Use advanced statistical /simulation tools and develop codes (python or another object oriented language) for data analysis , simulation, and developing modeling algorithms • Lead and coordinate simulation efforts between internal teams and outside vendors to develop optimal solutions for the network, including equipment specification, material flow control logic, process design, and site layout • Deliver results according to project schedules and quality A day in the life 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!