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 (vertical)
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.

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The Central AWS Econ team is dedicated to bringing the most trustworthy evidence-based analysis to the most strategic decisions for AWS leadership.Our studies impact strategic investments, service business model, resource allocation, product priorities and pricing models, go-to-market motions and more.This economist role partners with AWS business leaders across the organization to define and deliver on economic questions that guide their most strategic decisions. The successful candidate will be a problem solver who enjoys diving into data, is excited by difficult modeling challenges and ambiguous starting points, and possesses strong communication skills to effectively interface and collaborate with product, finance, planning and business teams.Specific questions include developing supporting economics for new business model, evaluating the relationship between short and long term growth, mapping and affecting the customer journey through different AWS products and cloud technologies.The Central AWS Econ team is dedicated to answering these (and many more) questions using quantitative, economic and statistical methods.Key Responsibilities:· Frame and conduct economic studies, from question definition to and communicating practical implications to senior leadership· Develop new repeatable data analysis pipelines to be used by non-economists
US, CA, Virtual Location - California
Are you passionate about leveraging your data science skills to make impact at scale? Do you enjoy developing innovative algorithms, optimization and predictive models to generate insights and recommendations that will be used by millions of Amazon selling partners and FBA operation teams to drive customer impact?Over 2 million Sellers in 10 countries list their products for sale on the Amazon Marketplace. To meet our sellers’ needs, our smart and customer-obsessed employees are constantly innovating and building on new ideas. Fulfillment by Amazon (FBA) is an Amazon service for our sellers. FBA Inbound analytics and data science team partners with FBA inbound product management team to optimize supply chain cost and lead time variability by influencing right trade-offs among cost, speed and FBA supply network capacity.We are looking for a motivated Data Scientist to build, optimize and productionize cutting edge machine learning models. A successful candidate will have strong quantitative, data mining, statistical modeling, machine learning skills and is comfortable facilitating ideation and working from concept through to execution. The position will partner with Product Management, Engineering, Supply Chain optimization and Finance teams to enhance short term and long term business use cases that leverage a range of data science methodologies to solve complex problems for the global FBA Inbound network.A qualified candidate must have demonstrated ability to develop and manage medium to large-scale models and methodologies that are statistically grounded but also functional and practical. Must possess strong written and verbal communication skills, proven ability to engage and collaborate with customers to drive improvements. Possess high intellectual curiosity with ability to quickly learn new concepts/frameworks, algorithms and technology.Key responsibilities of FBA Inbound data scientist include the following:· Research machine learning algorithms and implement by tailoring to FBA business problems· Manipulate/mine data from large databases (Redshift, SQL Server) and create automated pipeline for model training data sets· Improve model usability by analyzing customer behavior and by gathering requirements from business owners and other tech teams.· Create and track accuracy and performance of model predictions/recommendations. Retrain models to maximize business impact· Foster culture of continuous engineering improvement through mentoring, feedback, and analysis.· Lead setting up of machine learning infrastructure and processes for team to collaborate and share codeTo help describe some of our challenges, we created a short video about Supply Chain Optimization at Amazon - is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation / Age
US, WA, Seattle
Amazon’s eCommerce Foundation (eCF) organization provides the core technologies that drive and power the Amazon website and the consumer experience. Millions of customer page views and orders per day are enabled by the systems eCF builds from the ground up. eCF Data enables business analytics and insights, providing data and data curation capabilities to thousands of internal and external customers worldwide.Amazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading machine learning services. As a scientist in the BDT team, you'll partner with technology and business teams to build services that surprise and delight our customers. You will be working with petabytes of structured and unstructured data to help our customers derive critical insights and solve real-world problems. You'll design and implement cutting-edge distributed ML services from the ground up, design and run experiments, research new algorithms, and find new ways of optimizing the risk, profitability, and customer experience for a wide variety of business segments across Amazon. As part of this group, you will have the chance to work with a large team of thought leaders, engineers, and scientists in the distributed computing, machine learning, and business intelligence fields.Applied science at Amazon is a fast growing field. This is a highly technical role that requires substantial cross-disciplinary interaction with software engineers, product managers, solution architects, business intelligence engineers, and other scientists. Besides theoretical analysis and innovation, you will work closely with software engineers to put your research, designs, and algorithms into practice. You will also work on cross-disciplinary efforts with other scientists and engineers at Amazon to establish scalable, efficient, automated processes for large-scale data analysis, ML model development, and model validation.We’re looking for top scientists capable of using ML, computer science, distributed systems, and other techniques to design, implement, and evangelize state-of-the-art solutions for previously-unsolved problems.
US, VA, Arlington
Global Talent Management (GTM) is a central Human Resource (HR) team responsible for creating and evolving Amazon’s human capital and talent products and processes. The GTM Science team is a growing interdisciplinary team within GTM that develops evidence-based products and services that power the growth and development of Amazon’s talent across all of our businesses and locations around the world.GTM Science exists to propel GTM and Amazon HR toward being the most scientific HR organization on earth. Our mission is to use science to assist and measurably improve every talent decision made at Amazon. We do this by discovering signals in workforce data, integrating those signals and behavioral recommendations into Amazon’s talent products, and helping Amazonians make high-judgement decisions that raise the bar on talent. Our multi-disciplinary approach covers an array of capabilities, including: data engineering, business intelligence and analytics, research and behavioral sciences, data science, and applied sciences such as economics and machine learning.We are looking for a dynamic leader to join our leadership team. Reporting directly to the Director of GTM Science, you will lead a team of researchers and analysts primarily responsible for handling high-priority senior-leadership research requests that require scientific rigor and agility. You will be responsible for building mechanisms to scale collaborations across all areas of GTM Science/Product/Tech and to build project-based partnerships with HR Line Analytics and COE teams. Your approach balances scientific rigor and pragmatism, in order to deliver results at the speed of business decision-making. You and your team thrive on quickly framing open-ended business requests into an actionable research plan and reporting your results to the highest levels of leadership, to meaningfully shape Talent processes, policies, and programs in areas such as: Diversity & Inclusion, Flexible Work, Talent Mobility, Talent Evaluation, Talent Retention, Performance Management.
US, CA, Sunnyvale
Want to build the future of music and audio entertainment?Imagine being part of an agile team, where your ideas have the potential to reach millions. Envision working within a startup atmosphere, while being able to leverage the resources of a Fortune-500 company. Picture working on bleeding-edge consumer-facing products, where every team member is a critical voice in the decision-making process. Welcome to Amazon Music’s New Projects team.Our team builds new experiences for Amazon Music listeners. We help our customers discover up-and-coming creators, while also having access to their favorite music and podcasts. We build systems that are distributed around the world, spanning our music apps, web player, and voice-forward experiences on mobile and Amazon Echo devices, powered by Alexa. Amazon Music products support our mission of delivering audio entertainment in new and exciting ways that listeners love.Amazon Music’s New Projects team is looking for founding team members across a variety of functions, including software engineering/development, product, marketing, design, and more. Come make history, as we launch new projects for millions of listeners.
US, MA, Boston
Data Scientist: Elastic Block Store Data Services (AWS)Come and build the future with us as we change the way customers move and transform their EBS data at never before seen scale.EBS customers frequently need to move or transform their underlying data whether its accelerating volume creation from snapshots using Fast Snapshot Restore, increasing their database storage or changing their volume type through Elastic volumes, or encrypting their volumes using AWS managed keys. Our team creates solutions that enable and simplify EBS customer workflows.Building a High-Performing & Inclusive Team CultureYou should be passionate about working with a world-class team that welcomes, celebrates, and leverages a diverse set of backgrounds and skillsets to deliver results. Driving results is your primary responsibility, and doing so in a way that builds on our inclusive culture is key to our long term success.Work/Life BalanceEBS Data Services values work-life balance. On normal days, our entire team is co-located in the Boston office, but we’re also flexible when people occasionally need to work from home. We generally keep core available hours from 10am to 4pm. Some of the team is available earlier and the rest of us work a little later.Energizing and Interesting Technical ProblemsYou will work in partnership with engineers on the team to build and operate large scale systems that move and transform customer volume data and accelerate access to their data. You’ll be working to provide solutions to both internal and external customers and engage deeply with other teams within EBS, S3, EC2, and many other services. It’s humbling and energizing to provide data movement solutions to customers at AWS scale.Mentorship & Career GrowthWe’re committed to the growth and development of every member of EBS Data Services, and that includes our engineers. You will have the opportunity to contribute to the culture and direction of the entire EBS org and deliver initiatives that will improve the life of all of our teams.EBS Data Services is a growth environment - we’re hiring and scaling rapidly to meet the needs of our customers. You’ll have the opportunity to grow your scope of influence naturally as we scale and work on solutions that impact some of Amazon's largest customers.
US, MA, Virtual Location - Massachuset
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun.The Research and Advanced Development team at Amazon Robotics is seeking interns with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects, including allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, and learning how to grasp all the products Amazon sells. We are seeking internship candidates with backgrounds in computer vision, machine learning, resource allocation, discrete optimization, search, and planning/scheduling. You will be challenged intellectually and have a good time while you are at it!