Careers

The Selling Partner Experience (SPX) organization strives to make Amazon the best place for Selling Partners to do business. The SPX Science team is building an AI-powered conversational assistant to transform the Selling Partner experience. The Selling Assistant is a trusted partner and a seasoned advisor that’s always available to enable our partners to thrive in Amazon’s stores. It takes away the cognitive load of selling on Amazon by providing a single interface to handle a diverse set of selling needs. The assistant always stays by the seller's side, talks to them in their language, enables them to capitalize on opportunities, and helps them accomplish their business goals with ease. It is powered by the state-of-the-art Generative AI, going beyond a typical chatbot to provide a personalized experience to sellers running real businesses, large and small. Do you want to join an innovative team of scientists, engineers, product and program managers who use the latest Generative AI and Machine Learning technologies to help Amazon create a delightful Selling Partner experience? Do you want to build solutions to real business problems by automatically understanding and addressing sellers’ challenges, needs and opportunities? Are you excited by the prospect of contributing to one of Amazon’s most strategic Generative AI initiatives? If yes, then you may be a great fit to join the Selling Partner Experience Science team. Key job responsibilities - Use state-of-the-art Machine Learning and Generative AI techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed. - Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions. - Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features. - Establish scalable, efficient, automated processes for large scale data analyses, model benchmarking, model validation and model implementation. - Research and implement novel machine learning and statistical approaches. - Participate in strategic initiatives to employ the most recent advances in ML in a fast-paced, experimental environment. About the team Selling Partner Experience Science is a growing team of scientists, engineers and product leaders engaged in the research and development of the next generation of ML-driven technology to empower Amazon's Selling Partners to succeed. We draw from many science domains, from Natural Language Processing to Computer Vision to Optimization to Economics, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. We are focused on building seller facing AI-powered tools using the latest science advancements to empower sellers to drive the growth of their business. We strive to radically simplify the seller experience, lowering the cognitive burden of selling on Amazon by making it easy to accomplish critical tasks such as launching new products, understanding and complying with Amazon’s policies and taking actions to grow their business.

About Amazon Advertising: Amazon Advertising operates at the intersection of eCommerce and advertising, offering a rich array of digital display advertising solutions with the goal of helping our customers find and discover anything they want to buy. We help advertisers of all types to reach Amazon customers on Amazon.com, across our other owned and operated sites, on other high quality sites across the web, and on millions of mobile devices. We start with the customer and work backwards in everything we do, including advertising. If you’re interested in joining a rapidly growing team working to build a unique, world-class advertising group with a relentless focus on the customer, you’ve come to the right place. About our team: Our team, CreativeX optimizations, is responsible for tailoring the visual experience of ads to each context in real time. To accomplish this, we are investing in latent-diffusion models, large language models (LLM), reinforced learning (RL), Computer Vision, and related methods. Key job responsibilities We are looking for talented Applied Scientists who are adept at a variety of skills, especially with reinforcement learning and recommendations, and familiarity with LLMs, latent diffusion, or related foundational models that will accelerate our plans to dynamically optimize ad creatives on behalf of advertisers. Every member of the team is expected to build customer (advertiser) facing features, contribute to the collaborative spirit within the team, publish, patent, and bring cutting edge research to raise the bar within the team.

Join the Worldwide Sustainability (WWS) organization where we capitalize on our size, scale, and inventive culture to build a more resilient and sustainable company. WWS manages our social and environmental impacts globally, driving solutions that enable our customers, businesses, and the world around us to become more sustainable. Sustainability Science and Innovation is a multi-disciplinary team within the WW Sustainability organization that combines science, analytics, economics, statistics, machine learning, product development, and engineering expertise to identify, evaluate and/or develop new science, technologies, and innovations that aim to address long-term sustainability challenges. We are seeking an experienced Senior Applied Scientist to play a key role on our team. In this role, you will leverage your breadth of expertise in data science methodologies, statistical modeling, and scientific programming to analyze complex datasets, build scientific tools, and inform sustainability strategies across carbon, waste, and water management. The successful applicant will lead by example, pioneering science-vetted data-driven approaches, and working collaboratively to implement strategies that align with Amazon’s long-term sustainability vision. You will be at the forefront of exploring and resolving complex sustainability issues, bringing innovative ideas to the table, and making meaningful contributions to projects across SSI’s portfolio. This role not only demands technical expertise but also a strategic mindset and the agility to adapt to evolving sustainability challenges through self-driven learning and exploration. Key job responsibilities - Own the design and development of scientific scripts to solve complex and ambiguous problems, and extract strategic learnings from large datasets. - Effectively influence stakeholders across partner teams through strong communication, and high-quality technical artifacts. - Professionally communicate your work to senior business leaders and the broader science community. - Work closely with software engineering teams to implement your scientific models. - Lead early-stage strategic sustainability initiatives and effectively learn from, collaborate with, and influence stakeholders to scale-up high-value initiatives. About the team Diverse Experiences: World Wide Sustainability (WWS) values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Inclusive Team Culture: It’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth: We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance: We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Join us in the evolution of Amazon’s Seller business! The Selling Partner Growth organization is the growth and development engine for our Store. Partnering with business, product, and engineering, we catalyze SP growth with comprehensive and accurate data, unique insights, and actionable recommendations and collaborate with WW SP facing teams to drive adoption and create feedback loops. We strongly believe that any motivated SP should be able to grow their businesses and reach their full potential supported by Amazon tools and resources. We are looking for a Senior Applied Scientist to lead us to identify data-driven insight and opportunities to improve our SP growth strategy and drive new seller success. As a successful applied scientist on our talented team of scientists and engineers, you will solve complex problems to identify actionable opportunities, and collaborate with engineering, research, and business teams for future innovation. You need to have deep understanding on the business domain and have the ability to connect business with science. You are also strong in ML modeling and scientific foundation with the ability to collaborate with engineering to put models in production to answer specific business questions. You are an expert at synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication. You will continue to contribute to the research community, by working with scientists across Amazon, as well as collaborating with academic researchers and publishing papers (www.aboutamazon.com/research). Key job responsibilities As a Sr. Applied Scientist in the team, you will: - Identify opportunities to improve SP growth and translate those opportunities into science problems via principled statistical solutions (e.g. ML, causal, RL). - Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in MLOps. - Design and lead roadmaps for complex science projects to help SP have a delightful selling experience while creating long term value for our shoppers. - Work with our engineering partners and draw upon your experience to meet latency and other system constraints. - Identify untapped, high-risk technical and scientific directions, and simulate new research directions that you will drive to completion and deliver. - Be responsible for communicating our science innovations to the broader internal & external scientific community.

Amazon.com is seeking a Manager to lead a science team within Customer Forecasting and Valuation (CFV). We own a set of primary decision metrics at Amazon, leveraging causal machine-learning models to predict the long-term impact of customer actions. These metrics drive several investment and launch decisions across Amazon. This fast-paced, cross-disciplinary team of economists and scientists leverages advanced machine learning, statistics, and economics to solve complex problems like measuring the long-term causal effects of Amazon initiatives. CFV is part of the Customer Behavior Analytics (CBA) organization, which is responsible for developing the architecture, design, and implementation of tools used to understand customer behavior and value generation across the company's Retail business. As a manager within CFV, you will lead and collaborate with Applied Scientists, Economists, and Data Scientists to work backwards from customer needs and translate product ideas into concrete deliverables. This will involve inventing scalable causal measurement solutions that provide highly accurate and actionable insights, and drive improvement in key customer lifetime value metrics. You will interface directly with product owners, senior scientists/economists, and business leadership to create multi-year research and product agendas that drive step-change growth. Working with massive datasets spanning billions of customer transactions, you will partner closely with a dedicated engineering team to uncover insights that propel Amazon's Retail business forward. The role will also be an important collaborator with other science teams at CBA and Stores. The ideal candidate will have experience with machine learning models, causal inference, and a strong background in applied science, economics, and engineering. You should also possess creativity, curiosity, and excellent judgment to thrive in an environment of ambiguity. If you are seeking an opportunity to drive innovation, solve real-world problems using advanced analytics, and grow your career over time, this role on Amazon's industry-leading CBA team may be the perfect fit.

The Returns Economics Intelligence team brings together economists, data scientists, data analysts, and business intelligence engineers to deliver innovative research and products that discover and surface returns-influencing behavior, trends and their root causes. We leverage a range of scientific approaches such as causal modeling, structural and choice modeling, time series, ML, and optimization models to yield tangible insights targeted at reducing the cost of returns and concessions without slowing down the Amazon flywheel. We are looking for a detail-oriented, organized and responsible Economist intern with strong skills in time series and macroeconomic modeling. We are a new team at Amazon and this is a great opportunity to be on the leading edge. Roughly 85% of interns from previous cohorts have converted to full time economist employment at Amazon. If you are interested, please apply and send your CV to our mailing list at econ-internship@amazon.com.

Amazon's Pricing & Promotions Science is seeking a driven Applied Scientist to harness planet scale multi-modal datasets, and navigate a continuously evolving competitor landscape, in order to regularly generate fresh customer-relevant prices on billions of Amazon and Third Party Seller products worldwide. We are looking for a talented, organized, and customer-focused applied researchers to join our Pricing and Promotions Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon. This role requires an individual with exceptional machine learning and reinforcement learning modeling expertise, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator, who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment. Key job responsibilities - See the big picture. Understand and influence the long term vision for Amazon's science-based competitive, perception-preserving pricing techniques - Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale - Stay informed. Establish mechanisms to stay up to date on latest scientific advancements in machine learning, neural networks, natural language processing, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems - Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. - Successfully execute & deliver. Apply your exceptional technical machine learning expertise to incrementally move the needle on some of our hardest pricing problems. A day in the life We are hiring an applied scientist to drive our pricing optimization initiatives. The Price Optimization science team drives cross-domain and cross-system improvements through: - invent and deliver price optimization, simulation, and competitiveness tools for Sellers. - shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. - Promotion optimization initiatives exploring CX, discount amount, and cross-product optimization opportunities. - Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into many partner-team architectures, and is highly relevant to the customer, therefore this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication. About the team About the team: the Pricing Discovery and Optimization team within P2 Science owns price quality, discovery and discount optimization initiatives, including criteria for internal price matching, price discovery into search, p13N and SP, pricing bandits, and Promotion type optimization. We leverage planet scale data on billions of Amazon and external competitor products to build advanced optimization models for pricing, elasticity estimation, product substitutability, and optimization. We preserve long term customer trust by ensuring Amazon's prices are always competitive and error free.

Are you a MS or PhD student interested in a 2025 Internship in the field of machine learning, deep learning, speech, robotics, computer vision, optimization, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process 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 must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact, visionary person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Luxembourg, Netherlands, Poland, Romania, Spain, UAE, and UK). Please note these are not remote internships.

As it strives to be Earth's most customer-centric company, Amazon has grown its Last Mile delivery network, accelerating customer delivery times and providing innovation to customers. Amazon’s Last Mile programs deliver to homes, businesses, Amazon Lockers, and even cars all over the world. This network is powered by thousands of small businesses and hundreds of thousands of drivers that leverage Amazon's technology to deliver millions of smiles to customers each day. The Last Mile Economics Team draws from a wide range of technical skillsets in economics, statistics, machine learning, and operations research to develop innovative forecasting and optimization tools to help scale this exponentially growing logistics network. This role affords an opportunity to work on large, complex, and technically challenging problems, while directly contributing to driving an improved experience for Amazon customers and our delivery partners. We are looking for candidates with strong skills in Optimization (Mixed Integer Programming, Dynamic Programming), as well as solid skills in Python coding and data collection and analysis. Some background in Machine Learning (Forecasting, Reinforcement Learning), and Economics would be helpful too. The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail, an ability to work in a fast-paced and ever-changing environment, and a desire to help shape the overall business. Key job responsibilities • Design and develop advanced mathematical, optimization models, apply them to define strategic and tactical needs, and drive the appropriate business and technical solutions in the areas of delivery planning, supply chain optimization, pricing, incentives, and capacity planning. • Apply mathematical optimization and control techniques (linear, quadratic, SOCP, robust, stochastic, dynamic, mixed-integer programming, network flows, nonlinear, nonconvex programming, decomposition methods, model predictive control) and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software. • Research, prototype, simulate, and experiment with these models by using modeling languages such as Python, MATLAB, Mosel or R; participate in the production level deployment. • Create, enhance, and maintain technical documentation • Present to other Scientists, Product, and Software Engineering teams, as well as Stakeholders. • Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans. • Influence organization's long-term roadmap and resourcing, onboard new technologies onto Science team's toolbox, mentor other Scientists.

The Sr. Applied Scientist will be responsible for building advanced time series forecasting models for Amazon Devices using deep learning techniques and state-of-the-art sequence modeling approaches. They will develop scalable, accurate predictive models leveraging RNNs, LSTMs, and Transformer architectures to drive key decision making that supports critical business decisions (including pricing, promotions and supply chain) for all of Amazon consumer hardware product lines WW. Collaborating with cross-functional teams, they will integrate these models into operational systems to enhance data-driven decision-making and optimize business outcomes. Strong expertise in time series analysis, deep learning frameworks, and computational efficiency will be key to success in this role. Key job responsibilities • Build and implement advanced forecasting models using deep learning techniques, with expertise in time series analysis (trends, seasonality, stationarity) and sequence modeling architectures (RNNs, LSTMs, GRUs, Transformers) to drive decisions for pricing, promotions, and supply chain optimization. • Write efficient, scalable production code in Python, utilizing NumPy, Pandas, and deep learning frameworks (TensorFlow/Keras, PyTorch) to develop and deploy models, while collaborating with data engineers to ensure smooth integration into real-time systems. • Continuously optimize models through sophisticated feature engineering, hyperparameter tuning, and GPU acceleration techniques, while implementing appropriate preprocessing strategies for handling missing values, outliers, and data normalization. • Evaluate and communicate model performance using industry-standard metrics (MAE, RMSE, MAPE), create compelling visualizations using Matplotlib, and provide actionable recommendations to cross-functional teams for pricing, promotions, and supply chain strategies.