Garrett van Ryzin
Garrett van Ryzin joined Amazon's Supply Chain Optimization Technologies organization in August as a distinguished scientist.
Credit: Jesse Winter/Cornell University

How distinguished scientist Garrett van Ryzin is optimizing his time at Amazon

van Ryzin is focusing on driving innovations in areas ranging from inventory management to last-mile delivery.

Amazon announced in August 2020 that Garrett van Ryzin would be joining the company’s Supply Chain Optimization Technologies (SCOT) organization as a distinguished scientist. SCOT is responsible for designing, building, and operating the Amazon supply chain. SCOT systems manage inventory for the millions of items on Amazon, compute accurate delivery expectations for customer orders, and drive meaningful changes to Amazon’s fulfillment center network so that customers receive their packages in the most efficient way possible.

Prior to Amazon, van Ryzin was a professor of Operations, Technology and Information Management at Cornell Tech, and previously the Paul M. Montrone Professor of Decision, Risk, and Operations at the Columbia University Graduate School of Business.  His university research work has focused on algorithmic pricing, demand modeling, and stochastic optimization.

van Ryzin was also the head of marketplace optimization at ridesharing companies Lyft and Uber, where he led teams that developed models for a variety of functions, such as optimally dispatching drivers to riders, and developing pricing models and driver pay systems that improve market efficiency. Interestingly, van Ryzin’s paper that he wrote while pursuing his PhD at MIT “A Stochastic and Dynamic Vehicle Routing Problem in the Euclidean Plane” imagined a world of on-demand transportation as far back as 1991.

During his career, van Ryzin’s work on complex revenue management problems has enabled businesses across diverse industry sectors to get the most out of their limited capacity. To give just a few examples, van Ryzin’s research has enabled airlines to make a series of large-scale, dynamic and sequential decisions to determine the optimal price of a ticket at a particular moment in time. Retail companies have used similar dynamic optimization to manage inventory levels and prices for different products to maximize revenue.

What I find particularly interesting are problems that move beyond the constraints of optimizing within the system, to actually redesigning the system itself. 
Garrett van Ryzin

However, at Uber and Lyft van Ryzin tackled a new business environment, where revenue maximization wasn’t the primary goal. Instead, van Ryzin’s teams focused on optimizing more immediate metrics that were vital to the very survival of their services: service reliability, driver productivity, and growth.

For example, having a sufficient number of idle drivers at any given time is critical to maintaining throughput in ridesharing services. Surge pricing, a mechanism that van Ryzin’s team at Uber optimized, maintains an efficient level of idle drivers and encourages more drivers to get on the street during peak hours when they are needed the most.

van Ryzin sees technology-enabled service providers — be it at a ridesharing company like Lyft or the Fulfilled by Amazon (FBA) service — as transformational.  Only a few decades ago, businesses like these weren’t viable ways to organize service delivery due to high transaction costs and lack of real-time information. However, technology has radically improved information exchange and reduced transaction costs, which allows independent sellers to sell their products on Amazon much more efficiently than they could on their own.

In this interview, van Ryzin spoke about the different facets of market optimization, the intricacies of making automated decisions at scale, managing system complexity using approximation and decomposition ideas, and why he joined Amazon.

Q. What are the different elements of optimization?

I’d like to think of optimization being made up of human, technical and operational elements.

At a human level, the understanding of behavioral economics is absolutely critical. You have to create the right incentives for both suppliers and buyers to drive efficiencies. This is especially important for companies like Amazon that have many buyers and sellers participating and a high degree of decentralized activity. 

In addition to the human considerations, you also must develop a deep understanding of the technical elements of how these marketplaces work – the capabilities and limitation of the technology – which in turn allows you to gain insights into what structural changes are possible.

Finally, building services like Amazon that provide physical goods and services is a much more complicated endeavor than developing a service for trading virtual entities like stocks or mutual funds. To give just one example, at Amazon we are shipping actual, physical goods. This means the underlying physics of the infrastructure and the different operational elements are critical. So you must also think about your service in terms of factors like product weight and size, labor requirements, storage capacity, inventory levels, and lead times.

From a scientific perspective, there are several open questions in all three elements of market optimization. A fundamental one is determining the best approach to take to develop models to drive efficiency.

One approach is to develop structural models from first principles. For example, you could make an assumption that consumers are utility maximizers, develop a utility function and identify the parameters that constitute this utility function.

Garrett van Ryzin
Garrett van Ryzin, Amazon distinguished scientist

You could also take a radically different approach and build models based only on the underlying data – where you draw inferences from what the data alone tells you. Here, you’re not worrying about why something happened. Rather, you can use ideas from machine learning to estimate and refine predictive models without trying to understand the underlying mechanics.

What I find particularly interesting are problems that move beyond the constraints of optimizing within the system, to actually redesigning the system itself.  The ‘Wait and Save’ feature my group developed at Lyft is a good example. This product allows riders to opt into waiting for ten to fifteen minutes for a ride rather than having all rides be on-demand. In exchange for waiting, riders get a lower price. On the technology side, what we are doing here is actually changing the product in order to make the marketplace more efficient. I’ve always found there’s a lot more leverage in changing a system rather than optimizing within a fixed system.  It’s a lot trickier though because big structural changes often mean you have to get users comfortable with entirely new products or a completely new way of using the system.

Q. How do you account for the uncertainty and complexity inherent in large systems?

Approximation is at the heart of optimization because you can never fully represent the full complexity of a real-world trading system. For example, if a consumer places an order on Amazon, you have to make several sequential decisions with complex interactions.  Which fulfillment center should I take that order from? Should I place the items in the same box or should I pack them in different boxes? How will fulfilling this order impact the availability of inventory for the next order that comes in for that product? And how will it affect the available capacity of my local delivery assets?

You can develop approximation models by using a rolling horizon approach. This involves taking a best guess for what the future entails, and then updating your estimate for the future as and when you get new information. Or you could do something that’s far more sophisticated: build simulations of the future, and use sampling techniques to guide your decisions. You can also utilize reinforcement learning where you fit value functions to historical actions to arrive at decisions that are continually refined based on data.

Decomposition is also an important strategy for dealing with the interconnectedness of the different elements of the system. In large systems such as Amazon, everything is related to everything else. Supply affects costs, which affects pricing, which in turn affects demand, which affects dispatch, and so on. Ideally, you’d want to arrive at decisions by taking the whole system into account. However, the size of any real-world system makes this impossible. Any model you arrive at will be too complex, and you’d require a large amount of time to compute anything reasonable.

I’ve always been attracted to the idea of helping drive innovations to get people the basic, physical necessities that are essential to how they live.
Garrett van Ryzin

This is where decomposition comes in. You can break the system down into individual components – such as dispatch models, pricing models, inventory models and so on. The challenge here is to get these different models to collaborate. You don’t want scenarios where they are working at cross purposes with each other. For example, you don’t want one model trying to get rid of an item and have another model actively trying to replace it. In cases like these, you can drive coordination between different models using an internal price or some other mechanism that’s common to all the models.

These are just some of the trickiest issues in optimization, and I’m excited to be at Amazon where a lot of the innovation in these areas is taking place.

Q. Why did you decide to join Amazon?

I’ve always admired Amazon as a company because of its incredible track record of innovation across so many areas. I remember shopping at Amazon when they just sold books. And today, you have Amazon Studios, AWS, Amazon Devices, Alexa and even Project Kuiper where Amazon is putting up over 3,000 satellites in space.

Amazon is a company that excels at understanding economic opportunity and then building products and services that customers value. I’ve only been here for a few months, but I can already see how the company’s unique culture helps it be so successful across so many areas.

I also admire the company’s long-term perspective. Amazon doesn’t make decisions based on driving quarter-over-quarter performance. Amazon is willing to stick with ideas for many years. This appeals to me as a scientist as in my experience, sticking with the right idea over the long term is essential to making fundamental breakthroughs.

At SCOT, I’m excited to have the opportunity to contribute across so many areas, from FBA to last-mile delivery. Over the last few months, Amazon has helped so many people across the world get essential items during the pandemic. I’ve always been attracted to the idea of helping drive innovations to get people the basic, physical necessities that are essential to how they live.

Related content

US, CA, Santa Clara
About Amazon Health Amazon Health’s mission is to make it dramatically easier for customers to access the healthcare products and services they need to get and stay healthy. Towards this mission, we (Health Storefront and Shared Tech) are building the technology, products and services, that help customers find, buy, and engage with the healthcare solutions they need. Job summary We are seeking an exceptional Applied Scientist to join a team of experts in the field of machine learning, and work together to break new ground in the world of healthcare to make personalized and empathetic care accessible, convenient, and cost-effective. We leverage and train state-of-the-art large-language-models (LLMs) and develop entirely new experiences to help customers find the right products and services to address their health needs. We work on machine learning problems for intent detection, dialogue systems, and information retrieval. You will work in a highly collaborative environment where you can pursue both near-term productization opportunities to make immediate, meaningful customer impacts while pursuing ambitious, long-term research. You will work on hard science problems that have not been solved before, conduct rapid prototyping to validate your hypothesis, and deploy your algorithmic ideas at scale. You will get the opportunity to pursue work that makes people's lives better and pushes the envelop of science. Key job responsibilities - Translate product and CX requirements into science metrics and rigorous testing methodologies. - Invent and develop scalable methodologies to evaluate LLM outputs against metrics and guardrails. - Design and implement the best-in-class semantic retrieval system by creating high-quality knowledge base and optimizing embedding models and similarity measures. - Conduct tuning, training, and optimization of LLMs to achieve a compelling CX while reducing operational cost to be scalable. A day in the life In a fast-paced innovation environment, you work closely with product, UX, and business teams to understand customer's challenges. You translate product and business requirements into science problems. You dive deep into challenging science problems, enabling entirely new ML and LLM-driven customer experiences. You identify hypothesis and conduct rapid prototyping to learn quickly. You develop and deploy models at scale to pursue productizations. You mentor junior science team members and help influence our org in scientific best practices. About the team We are the ML Science and Engineering team, with a strong focus on Generative AI. The team consists of top-notch ML Scientists with diverse background in healthcare, robotics, customer analytics, and communication. We are committed to building and deploying the most advanced scientific capabilities and solutions for the products and services at Amazon Health. We are open to hiring candidates to work out of one of the following locations: Santa Clara, CA, USA
US, WA, Seattle
We are designing the future. If you are in quest of an iterative fast-paced environment, where you can drive innovation through scientific inquiry, and provide tangible benefit to hundreds of thousands of our associates worldwide, this is your opportunity. Come work on the Amazon Worldwide Fulfillment Design & Engineering Team! We are looking for an experienced and senior Research Scientist with background in Ergonomics and Industrial Human Factors, someone that is excited to work on complex real-world challenges for which a comprehensive scientific approach is necessary to drive solutions. Your investigations will define human factor / ergonomic thresholds resulting in design and implementation of safe and efficient workspaces and processes for our associates. Your role will entail assessment and design of manual material handling tasks throughout the entire Amazon network. You will identify fundamental questions pertaining to the human capabilities and tolerances in a myriad of work environments, and will initiate and lead studies that will drive decision making on an extreme scale. .You will provide definitive human factors/ ergonomics input and participate in design with every single design group in our network, including Amazon Robotics, Engineering R&D, and Operations Engineering. You will work closely with our Worldwide Health and Safety organization to gain feedback on designs and work tenaciously to continuously improve our associate’s experience. Key job responsibilities - Collaborating and designing work processes and workspaces that adhere to human factors / ergonomics standards worldwide. - Producing comprehensive and assessments of workstations and processes covering biomechanical, physiological, and psychophysical demands. - Effectively communicate your design rationale to multiple engineering and operations entities. - Identifying gaps in current human factors standards and guidelines, and lead comprehensive studies to redefine “industry best practices” based on solid scientific foundations. - Continuously strive to gain in-depth knowledge of your profession, as well as branch out to learn about intersecting fields, such as robotics and mechatronics. - Travelling to our various sites to perform thorough assessments and gain in-depth operational feedback, approximately 25%-50% of the time. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
GB, London
Amazon Advertising is looking for a Data Scientist to join its brand new initiative that powers Amazon’s contextual advertising products. Advertising at Amazon is a fast-growing multi-billion dollar business that spans across desktop, mobile and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third party publishers; and extends across US, EU and an increasing number of international geographies. The Supply Quality organization has the charter to solve optimization problems for ad-programs in Amazon and ensure high-quality ad-impressions. We develop advanced algorithms and infrastructure systems to optimize performance for our advertisers and publishers. We are focused on solving a wide variety of problems in computational advertising like traffic quality prediction (robot and fraud detection), Security forensics and research, Viewability prediction, Brand Safety, Contextual data processing and classification. Our team includes experts in the areas of distributed computing, machine learning, statistics, optimization, text mining, information theory and big data systems. We are looking for a dynamic, innovative and accomplished Data Scientist to work on data science initiatives for contextual data processing and classification that power our contextual advertising solutions. Are you an experienced user of sophisticated analytical techniques that can be applied to answer business questions and chart a sustainable vision? Are you exited by the prospect of communicating insights and recommendations to audiences of varying levels of technical sophistication? Above all, are you an innovator at heart and have a track record of resolving ambiguity to deliver result? As a data scientist, you help our data science team build cutting edge models and measurement solutions to power our contextual classification technology. As this is a new initiative, you will get an opportunity to act as a thought leader, work backwards from the customer needs, dive deep into data to understand the issues, define metrics, conceptualize and build algorithms and collaborate with multiple cross-functional teams. Key job responsibilities * Define a long-term science vision for contextual-classification tech, driven fundamentally from the needs of our advertisers and publishers, translating that direction into specific plans for the science team. Interpret complex and interrelated data points and anecdotes to build and communicate this vision. * Collaborate with software engineering teams to Identify and implement elegant statistical and machine learning solutions * Oversee the design, development, and implementation of production level code that handles billions of ad requests. Own the full development cycle: idea, design, prototype, impact assessment, A/B testing (including interpretation of results) and production deployment. * Promote the culture of experimentation and applied science at Amazon. * Demonstrated ability to meet deadlines while managing multiple projects. * Excellent communication and presentation skills working with multiple peer groups and different levels of management * Influence and continuously improve a sustainable team culture that exemplifies Amazon’s leadership principles. We are open to hiring candidates to work out of one of the following locations: London, GBR
JP, 13, Tokyo
We are seeking a Principal Economist to be the science leader in Amazon's customer growth and engagement. The wide remit covers Prime, delivery experiences, loyalty program (Amazon Points), and marketing. We look forward to partnering with you to advance our innovation on customers’ behalf. Amazon has a trailblazing track record of working with Ph.D. economists in the tech industry and offers a unique environment for economists to thrive. As an economist at Amazon, you will apply the frontier of econometric and economic methods to Amazon’s terabytes of data and intriguing customer problems. Your expertise in building reduced-form or structural causal inference models is exemplary in Amazon. Your strategic thinking in designing mechanisms and products influences how Amazon evolves. In this role, you will build ground-breaking, state-of-the-art econometric models to guide multi-billion-dollar investment decisions around the global Amazon marketplaces. You will own, execute, and expand a research roadmap that connects science, business, and engineering and contributes to Amazon's long term success. As one of the first economists outside North America/EU, you will make an outsized impact to our international marketplaces and pioneer in expanding Amazon’s economist community in Asia. The ideal candidate will be an experienced economist in empirical industrial organization, labour economics, or related structural/reduced-form causal inference fields. You are a self-starter who enjoys ambiguity in a fast-paced and ever-changing environment. You think big on the next game-changing opportunity but also dive deep into every detail that matters. You insist on the highest standards and are consistent in delivering results. Key job responsibilities - Work with Product, Finance, Data Science, and Data Engineering teams across the globe to deliver data-driven insights and products for regional and world-wide launches. - Innovate on how Amazon can leverage data analytics to better serve our customers through selection and pricing. - Contribute to building a strong data science community in Amazon Asia. We are open to hiring candidates to work out of one of the following locations: Tokyo, 13, JPN
DE, BE, Berlin
Ops Integration: Concessions team is looking for a motivated, creative and customer obsessed Snr. Applied Scientist with a strong machine learning background, to develop advanced analytics models (Computer Vision, LLMs, etc.) that improve customer experiences We are the voice of the customer in Amazon’s operations, and we take that role very seriously. If you join this team, you will be a key contributor to delivering the Factory of the Future: leveraging Internet of Things (IoT) and advanced analytics to drive tangible, operational change on the ground. You will collaborate with a wide range of stakeholders (You will partner with Research and Applied Scientists, SDEs, Technical Program Managers, Product Managers and Business Leaders) across the business to develop and refine new ways of assessing challenges within Amazon operations. This role will combine Amazon’s oldest Leadership Principle, with the latest analytical innovations, to deliver business change at scale and efficiently The ideal candidate will have deep and broad experience with theoretical approaches and practical implementations of vision techniques for task automation. They will be a motivated self-starter who can thrive in a fast-paced environment. They will be passionate about staying current with sensing technologies and algorithms in the broader machine vision industry. They will enjoy working in a multi-disciplinary team of engineers, scientists and business leaders. They will seek to understand processes behind data so their recommendations are grounded. Key job responsibilities Your solutions will drive new system capabilities with global impact. You will design highly scalable, large enterprise software solutions involving computer vision. You will develop complex perception algorithms integrating across multiple sensing devices. You will develop metrics to quantify the benefits of a solution and influence project resources. You will validate system performance and use insights from your live models to drive the next generation of model development. Common tasks include: • Research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines • Work closely with software engineering teams to drive scalable, real-time implementations • Collaborate closely with team members on developing systems from prototyping to production level • Collaborate with teams spread all over the world • Track general business activity and provide clear, compelling management reports on a regular basis We are open to hiring candidates to work out of one of the following locations: Berlin, BE, DEU | Berlin, DEU
DE, BY, Munich
Ops Integration: Concessions team is looking for a motivated, creative and customer obsessed Snr. Applied Scientist with a strong machine learning background, to develop advanced analytics models (Computer Vision, LLMs, etc.) that improve customer experiences We are the voice of the customer in Amazon’s operations, and we take that role very seriously. If you join this team, you will be a key contributor to delivering the Factory of the Future: leveraging Internet of Things (IoT) and advanced analytics to drive tangible, operational change on the ground. You will collaborate with a wide range of stakeholders (You will partner with Research and Applied Scientists, SDEs, Technical Program Managers, Product Managers and Business Leaders) across the business to develop and refine new ways of assessing challenges within Amazon operations. This role will combine Amazon’s oldest Leadership Principle, with the latest analytical innovations, to deliver business change at scale and efficiently The ideal candidate will have deep and broad experience with theoretical approaches and practical implementations of vision techniques for task automation. They will be a motivated self-starter who can thrive in a fast-paced environment. They will be passionate about staying current with sensing technologies and algorithms in the broader machine vision industry. They will enjoy working in a multi-disciplinary team of engineers, scientists and business leaders. They will seek to understand processes behind data so their recommendations are grounded. Key job responsibilities Your solutions will drive new system capabilities with global impact. You will design highly scalable, large enterprise software solutions involving computer vision. You will develop complex perception algorithms integrating across multiple sensing devices. You will develop metrics to quantify the benefits of a solution and influence project resources. You will validate system performance and use insights from your live models to drive the next generation of model development. Common tasks include: • Research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines • Work closely with software engineering teams to drive scalable, real-time implementations • Collaborate closely with team members on developing systems from prototyping to production level • Collaborate with teams spread all over the world • Track general business activity and provide clear, compelling management reports on a regular basis We are open to hiring candidates to work out of one of the following locations: Munich, BE, DEU | Munich, BY, DEU | Munich, DEU
IT, MI, Milan
Ops Integration: Concessions team is looking for a motivated, creative and customer obsessed Snr. Applied Scientist with a strong machine learning background, to develop advanced analytics models (Computer Vision, LLMs, etc.) that improve customer experiences We are the voice of the customer in Amazon’s operations, and we take that role very seriously. If you join this team, you will be a key contributor to delivering the Factory of the Future: leveraging Internet of Things (IoT) and advanced analytics to drive tangible, operational change on the ground. You will collaborate with a wide range of stakeholders (You will partner with Research and Applied Scientists, SDEs, Technical Program Managers, Product Managers and Business Leaders) across the business to develop and refine new ways of assessing challenges within Amazon operations. This role will combine Amazon’s oldest Leadership Principle, with the latest analytical innovations, to deliver business change at scale and efficiently The ideal candidate will have deep and broad experience with theoretical approaches and practical implementations of vision techniques for task automation. They will be a motivated self-starter who can thrive in a fast-paced environment. They will be passionate about staying current with sensing technologies and algorithms in the broader machine vision industry. They will enjoy working in a multi-disciplinary team of engineers, scientists and business leaders. They will seek to understand processes behind data so their recommendations are grounded. Key job responsibilities Your solutions will drive new system capabilities with global impact. You will design highly scalable, large enterprise software solutions involving computer vision. You will develop complex perception algorithms integrating across multiple sensing devices. You will develop metrics to quantify the benefits of a solution and influence project resources. You will validate system performance and use insights from your live models to drive the next generation of model development. Common tasks include: • Research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines • Work closely with software engineering teams to drive scalable, real-time implementations • Collaborate closely with team members on developing systems from prototyping to production level • Collaborate with teams spread all over the world • Track general business activity and provide clear, compelling management reports on a regular basis We are open to hiring candidates to work out of one of the following locations: Milan, MI, ITA
ES, M, Madrid
Ops Integration: Concessions team is looking for a motivated, creative and customer obsessed Snr. Applied Scientist with a strong machine learning background, to develop advanced analytics models (Computer Vision, LLMs, etc.) that improve customer experiences We are the voice of the customer in Amazon’s operations, and we take that role very seriously. If you join this team, you will be a key contributor to delivering the Factory of the Future: leveraging Internet of Things (IoT) and advanced analytics to drive tangible, operational change on the ground. You will collaborate with a wide range of stakeholders (You will partner with Research and Applied Scientists, SDEs, Technical Program Managers, Product Managers and Business Leaders) across the business to develop and refine new ways of assessing challenges within Amazon operations. This role will combine Amazon’s oldest Leadership Principle, with the latest analytical innovations, to deliver business change at scale and efficiently The ideal candidate will have deep and broad experience with theoretical approaches and practical implementations of vision techniques for task automation. They will be a motivated self-starter who can thrive in a fast-paced environment. They will be passionate about staying current with sensing technologies and algorithms in the broader machine vision industry. They will enjoy working in a multi-disciplinary team of engineers, scientists and business leaders. They will seek to understand processes behind data so their recommendations are grounded. Key job responsibilities Your solutions will drive new system capabilities with global impact. You will design highly scalable, large enterprise software solutions involving computer vision. You will develop complex perception algorithms integrating across multiple sensing devices. You will develop metrics to quantify the benefits of a solution and influence project resources. You will validate system performance and use insights from your live models to drive the next generation of model development. Common tasks include: • Research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines • Work closely with software engineering teams to drive scalable, real-time implementations • Collaborate closely with team members on developing systems from prototyping to production level • Collaborate with teams spread all over the world • Track general business activity and provide clear, compelling management reports on a regular basis We are open to hiring candidates to work out of one of the following locations: Madrid, ESP | Madrid, M, ESP
US, CA, San Diego
The Private Brands team is looking for an Applied Scientist to join the team in building science solutions at scale. Our team applies Optimization, Machine Learning, Statistics, Causal Inference, and Econometrics/Economics to derive actionable insights. We are an interdisciplinary team of Scientists, Engineers, and Economists and primary focus on building optimization and machine learning solutions in supply chain domain with specific focus on Amazon private brand products. Key job responsibilities You will work with business leaders, scientists, and economists to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable optimization solutions and ML models. This is a unique, high visibility opportunity for someone who wants to have business impact, dive deep into large-scale problems, enable measurable actions on the consumer economy, and work closely with scientists and economists. As a scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. We are particularly interested in candidates with experience in predictive and machine learning models and working with distributed systems. Academic and/or practical background in Machine Learning are particularly relevant for this position. Familiarity and experience in applying Operations Research techniques to supply chain problems is a plus. To know more about Amazon science, Please visit https://www.amazon.science We are open to hiring candidates to work out of one of the following locations: San Diego, CA, USA | Seattle, WA, USA
LU, Luxembourg
Ops Integration: Concessions team is looking for a motivated, creative and customer obsessed Snr. Applied Scientist with a strong machine learning background, to develop advanced analytics models (Computer Vision, LLMs, etc.) that improve customer experiences We are the voice of the customer in Amazon’s operations, and we take that role very seriously. If you join this team, you will be a key contributor to delivering the Factory of the Future: leveraging Internet of Things (IoT) and advanced analytics to drive tangible, operational change on the ground. You will collaborate with a wide range of stakeholders (You will partner with Research and Applied Scientists, SDEs, Technical Program Managers, Product Managers and Business Leaders) across the business to develop and refine new ways of assessing challenges within Amazon operations. This role will combine Amazon’s oldest Leadership Principle, with the latest analytical innovations, to deliver business change at scale and efficiently The ideal candidate will have deep and broad experience with theoretical approaches and practical implementations of vision techniques for task automation. They will be a motivated self-starter who can thrive in a fast-paced environment. They will be passionate about staying current with sensing technologies and algorithms in the broader machine vision industry. They will enjoy working in a multi-disciplinary team of engineers, scientists and business leaders. They will seek to understand processes behind data so their recommendations are grounded. Key job responsibilities Your solutions will drive new system capabilities with global impact. You will design highly scalable, large enterprise software solutions involving computer vision. You will develop complex perception algorithms integrating across multiple sensing devices. You will develop metrics to quantify the benefits of a solution and influence project resources. You will validate system performance and use insights from your live models to drive the next generation of model development. Common tasks include: • Research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines • Work closely with software engineering teams to drive scalable, real-time implementations • Collaborate closely with team members on developing systems from prototyping to production level • Collaborate with teams spread all over the world • Track general business activity and provide clear, compelling management reports on a regular basis We are open to hiring candidates to work out of one of the following locations: Luxembourg, LUX