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.


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Job summaryMachine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab Applied Scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data and develop novel models to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will apply classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries· Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, TX, Austin
Job summaryMachine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab Applied Scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data and develop novel models to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will apply classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries· Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, MI, Detroit
Job summaryMachine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab Applied Scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data and develop novel models to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will apply classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries· Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, WA, Seattle
Job summaryAre you passionate about big data and machine learning? Do you enjoy solving complex analytical problems, build predictive analytics models and develop insights and recommendations at web scale? Do you want to make an impact in Amazon’s multi-billion-dollar Messaging business? Amazon’s Outbound Communication Services, OCS, owns foundational systems that power all of Amazon's customer-facing communication on Email, SMS, Push channels, and other emerging messaging applications. In 2020, we sent over 100 billion messages to our global customers on these channels! OCS builds self-governing and message-optimizing engines that leverage integrated Machine Learning, massive data processing and adaptive algorithms to deliver the best messages to Amazon’s customers, over the best channel, and at the right time. Our systems ensure best-in-class engagement experience, which spans transactional and marketing communications. We're building a top-notch team of engineers, applied scientists, data engineers, and engineering leaders. The problems we face are complex and interesting including information engineering, data mining of Big Data sets, and governing real-time messages at scale. We build large scale, distributed systems using multiple AWS services that we designed from the ground up.We are looking for a strong Applied Scientist to work backwards from customers, create models and develop insights, deliver impact, and drive growth of the OCS worldwide. As a Senior Applied Scientist on our team, you will be working with business stakeholders, product/program managers, developers and executives to deeply understand customer problems and priorities. You will form hypotheses, analyze the corpus of Outbound and Amazon data using statistical methods, build predictive models, generate recommendations to address a range of problems to optimize the value of the messages we send to our customers. These span developing insights on customer engagement trends, building models to inform message rates, channel selection, and defining segmentation for marketing, etc. Your expertise will enable us to enable new customer experiences while maintaining best-in-class customer experience. You will be comfortable with big data systems, intimately familiar with advanced machine learning and statistical methods, and experienced in applying these to solve business problems. You will have a strong bias towards customer obsession and delivering results while dealing with ambiguity in fast-paced dynamic environments.Roles and Responsibilities· Work with business teams, product/program managers, engineers and leadership to identify and prioritize customer and business problems· Translate these problems into specific analytical questions and form hypotheses that can be answered with available data using statistical methods or identify additional data needed in the master datasets to fill any gaps· Perform hand-on data analyses and modeling with huge datasets to develop insights and recommendations to inform decisions across the program· Design and run A/B experiments to validate the hypotheses and evaluate the impact of your optimizations and communicate your results to various stakeholders· Collaborate with engineers and product managers to build scalable solutions and new capabilities· Help on projects of high visibility across the organization and deliver business impact
US, MA, Cambridge
Job summaryThe Alexa Artificial Intelligence (AI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading Speech and Language technology. Our mission is to push the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), and Audio Signal Processing, in order to provide the best-possible experience for our customers.Key job responsibilitiesAs an Applied Scientist, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding.
US, WA, Bellevue
Job summaryAmazon’s Modeling and Optimization Team is looking for a senior science leader (Principal Research Scientist) to drive the development and optimization of the most complex transportation and fulfillment network in the world.The team is responsible for optimizing the global transportation and fulfillment network for Amazon.com and ensuring that the company is able to deliver our customers’ products to them as quickly, accurately, and cost effectively as possible. We design the network that delivers packages from fulfillment centers (FCs) to end customers, through both Amazon’s internal network as well as external partners, using ground and air methods. Optimizing these package flows requires designing the structure and operating parameters of the network, optimizing operations such as linehaul movements and sortation scheduling, and making the right capital investments in technology, buildings and equipment. It also requires partnering with peer research and product teams that drive inventory placement, selection, and forecasting, and incorporating information and algorithmic interfaces with those systems.We are seeking an experienced multi-disciplinary science leader, to lead the science development on projects in both developing algorithmic tools and driving long-term network strategy. In this role, you will work with and advise other research scientists in the team, develop your own scientific approaches, and partner with software and product teams as they deploy algorithmic solutions to our stakeholders. You will also partner with business leaders on projects that evaluate long-term strategic network choices, and present strategy papers to our senior-most leaders to drive long-term impact.To accomplish this, we expect you to have a strong research background in at least one of the following disciplines: operations research, computer science, operations management, statistics, or applied mathematics. You should also have business domain knowledge in transportation and distribution operations, along with some knowledge of inventory management theory and practice. You should have a strong publication record that demonstrates technical depth as well as consideration of practical aspects in your work. Experience partnering with companies on high-stakes R&D or consulting is a plus, but not a necessity.
US, VA, Arlington
Job summaryInterested in machine learning and AI? Can you envision a future where technology is driven primarily by smarter machines?The mission of AWS AI is to make machine learning easy, fast, and universal across all of our customers. Our world class platform provides the services that runs 85% of all on-cloud machine learning through performance optimizations, machine learning tools, and SDKs to democratize machine learning. Our customers include scientists, data analysts, and ML engineers all building and deploying models through either SageMaker or self-managed instances on EC2/EKS.The AWS Machine Learning Services group in Amazon AI is seeking applicants for an applied scientist position in the areas of fairness, bias, explainability, privacy, and model understanding in ML. As an applied scientist in AWS AI, you will be driving many scientific breakthroughs to help our customers understand and trust ML-based outcomes, and also working with software engineers to deploy these solutions to AWS customers. You will be working on the ML platforms and services that power over 85% of machine learning in the cloud, and help make machine learning fast, accessible, universal, and trustworthy for all our customers.We look for candidates with PhD in a relevant technical field (such as Algorithms, Machine Learning, AI, and Mathematics), preferably with interest / experience in explainability, fairness, privacy, and model understanding. We also encourage graduating PhD students as well as recent PhD graduates to apply. You will be working closely with software engineers so experience with cloud systems is a benefit. Our customers are deeply technical and the solutions we build for them are strongly coupled to technical feasibility. You must be able to thrive and succeed in an entrepreneurial environment, and not be hindered by ambiguity or competing priorities. This means you are not only able to develop and drive high-level strategic initiatives, but can also roll up your sleeves, dig in and get the job done. Ownership, high judgment, negotiation skills, ability to influence, analytical talent and leadership are essential to success in this role.For more information about AWS AI team, please see https://aws.amazon.com/machine-learning and https://aws.amazon.com/sagemaker.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
LU, Luxembourg
Job summaryWould you like to pioneer new technologies in machine learning while leveraging Amazon's heterogenous data sources and large-scale computing resources?As a data scientist, your work will help Amazon raise the bar on customer obsession by providing our Sellers with the best customized experiences.We are looking for a passionate, talented, and inventive Scientist with a strong machine learning background to join us to build industry-leading technology.The ideal candidate will have extensive experience in Science work, business analytics and have the aptitude to incorporate new approaches and methodologies while dealing with ambiguities in sourcing processes. Excellent business and communication skills are a must to develop and define key business questions and to build data sets that answer those questions. You should have a demonstrated ability to think strategically and analytically about business, product, and technical challenges. Further, you must have the ability to build and communicate compelling value propositions, and work across the organization to achieve consensus. This role requires a strong passion for customers, a high level of comfort navigating ambiguity, and a keen sense of ownership and drive to deliver results.
US, WA, Seattle
Come join the AWS Marketplace Team in our mission to change the way enterprise software is bought and sold! AWS Marketplace enables software sellers to reach all AWS customers, and it enables software buyers to easily discover, purchase and consume software. Our goals include enriching the platform to support more diverse selection, improving buyer and seller experience, and supporting co-sell opportunities between AWS and our partners. Our vision is to make AWS Marketplace the one stop shop for buying and selling software - we're building the app store for AWS.A day in the lifeIn this role, you’ll be utilizing your creative and critical problem solving skills to drive new projects from ideation to implementation. You will improve and innovate on existing high impact analytical projects. Your outputs will focus on our team’s critical goals and help influence decision makers.About the hiring groupAbout UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.Job responsibilitiesWe are looking for a Data Scientist to join our rapidly growing Seattle team. As a Data Scientist, you are able to use a range of data science methodologies to solve challenging business problems when the solution is unclear. You have a combination of business acumen, broad knowledge of statistics, deep understanding of ML algorithms, and an analytical mindset. You thrive in a collaborative environment, and are passionate about learning. Our team utilizes a variety of AWS tools such as Redshift, Sagemaker, Lambda, S3, and EC2 with a variety of skillsets in Tabular ML, NLP, Forecasting, Probabilistic ML and Causal ML. You will bring knowledge in many of these domains along with your own specialties and skillsets.
DE, BE, Berlin
Machine Learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.Machine Learning University’s (MLU) mission is to teach people to apply ML to business and operational challenges. In service of this goal, we are growing our presence outside the US, and are looking to hire in EMEA region. We believe strongly that a practical knowledge of ML can be taught broadly, and is a key skill for many builders to learn. Come join Amazon’s Machine Learning University and help spread knowledge of ML!We have built a team of passionate science educators with the demonstrated ability to explain ML to an audience of technical professionals, enhance curriculum, and collaborate with scientists across the company. You will teach practitioners how to train, tune, and deploy ML models to solve challenges in customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analtyics, and event detection among others. Practical experience with machine learning is key to ensure content is up-to-date, practical and engaging. This is a full-time role where you will constantly be challenged to learn and be curious about ML.As a Sr. MLU Applied Scientist on our team, you would have the following responsibilities:· Teach customers directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Develop open-source curriculum designed to provide a practical knowledge of ML. This includes lectures, videos, demos, and coding notebook assignments (find an article about our largest release here: https://www.amazon.science/latest-news/machine-learning-course-free-online-from-amazon-machine-learning-university)· Collaborate with scientists across Amazon to continuously understand and integrate advances in ML into the curriculum· Help drive high-level curriculum decisions to ensure alignment with students’ needs and the state-of-the-art domains of advancement in ML· Consult with customers on model development and deployment best practices by using computer science fundamentals· Engage in the interview process and otherwise develop, grow, and mentor junior scientistsThis team is comprised of Data Scientists, Applied Scientists and Instructional Designers to create first-in-class courses and learning assets in practical ML. The role may require travel up to 40% of the time (pending safety protocols and travel restrictions). We are currently recruiting for talented individuals for this role in the following cities: London, Cambridge, Tuebingen, Dublin, Amsterdam, Berlin, Paris, or Gdansk. Let’s bring ML to everyone and demystify and democratize technology together!Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfilment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We Hire and Develop the best so you can expect support in advancing your career ambitions and projects which will help you grow and develop your skills.
DE, BE, Berlin
Machine Learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.Machine Learning University’s (MLU) mission is to teach people to apply ML to business and operational challenges. In service of this goal, we are growing our presence outside the US, and are looking to hire in EMEA region. We believe strongly that a practical knowledge of ML can be taught broadly, and is a key skill for many builders to learn. Come join Amazon’s MLU and help spread knowledge of ML!We have built a team of passionate science educators with the demonstrated ability to explain ML to an audience of technical professionals, enhance curriculum, and collaborate with scientists across the company. You will teach practitioners how to train, tune, and deploy ML models to solve challenges in customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analtyics, and event detection among others. Practical experience with machine learning is key to ensure content is up-to-date, practical and engaging. This is a full-time role where you will constantly be challenged to learn and be curious about ML.As a MLU Applied Scientist on our team, you would have the following responsibilities:· Teach customers directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Develop open-source curriculum designed to provide a practical knowledge of ML. This includes lectures, videos, demos, and coding notebook assignments (find an article about our largest release here: https://www.amazon.science/latest-news/machine-learning-course-free-online-from-amazon-machine-learning-university)· Collaborate with scientists across Amazon to continuously understand and integrate advances in ML into the curriculum· Help drive high-level curriculum decisions to ensure alignment with students’ needs and the state-of-the-art domains of advancement in ML· Consult with customers on model development and deployment best practices by using computer science fundamentalsThis team is comprised of Data Scientists, Applied Scientists and Instructional Designers to create first-in-class courses and learning assets in practical ML. The role may require travel up to 40% of the time (pending safety protocols and travel restrictions). We are currently recruiting for talented individuals for this role in the following cities: London, Cambridge, Tuebingen, Dublin, Amsterdam, Berlin, Paris, or Gdansk. Let’s bring ML to everyone and demystify and democratize technology together!Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfilment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We Hire and Develop the best so you can expect support in advancing your career ambitions and projects which will help you grow and develop your skills.
US, WA, Seattle
Job summaryThe AWS Cryptography Group is looking for an Applied Scientist with knowledge of cryptographic computing technologies such as privacy preserving machine learning, fully and partially homomorphic encryption, secure multiparty computation, private information retrieval, and related technologies. You will use this knowledge to conceptualize how this technology can be integrated into internal infrastructure and public AWS services, develop prototypes, and provide customers with world-class security. The ideal candidate will have a strong understanding of cryptography, the ability to prototype solutions, and a passion to realize these technologies in AWS products and services. We encourage research and publication of results that apply to our customers most complex initiatives.We are seeking a candidate who is innovative, looking for new ideas everywhere. They should think big, and have bold ideas for new ways to serve customers.About UsAWS has the most services and more features within those services, than any other cloud provider–from infrastructure technologies like compute, storage, and databases–to emerging technologies, such as machine learning and artificial intelligence, data lakes and analytics, and Internet of Things. AWS Platform is the glue that holds the AWS ecosystem together. Whether its Identity features such as access management and sign on, cryptography, console, builder & developer tools, and even projects like automating all of our contractual billing systems, AWS Platform is always innovating with the customer in mind. The AWS Platform team sustains over 750 million transactions per second.A Culture of InclusionHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceWe put a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We provide both the opportunity to be mentored by seasoned leaders and to mentor junior researchers. We care about your career growth and strive to give you challenging responsibilities that will help you develop into a better-rounded applied scientist and give you the opportunity to advance your career to the next level.Learn more about Amazon on our Day 1 Blog: https://blog.aboutamazon.com/
US, VA, Arlington
Job summaryThe Group:The Database Services group provides rapidly growing, industry acclaimed cloud services in areas of big data platforms, data warehouses, analytics, and operational databases. The group is at the forefront of innovation in these areas producing world-class cloud services, which while large profitable businesses, are also in their infancy. They represent a large fraction of the AWS business, and continue to accelerate.The Team:Lake Formation is our fully managed service to simplify building and securing data lakes. It features Blueprints to ingest data from commons sources, ML transforms to clean and de-duplicate data and a unified security model with fine-grained permissions (at the database, table, and column level) that are applicable across a wide range of AWS analytic and ML services (Redshift Spectrum, Glue, EMR Spark, QuickSight).The Lake Formation team is looking for an Applied Scientist with experience in building secure, scalable solutions that delight customers. You will apply your knowledge to propose solutions, create software prototypes, and productize prototypes into production systems using software development tools and methodologies. In addition, you will support and scale your solutions to meet the ever-growing demand of customer use. You have strong verbal and written communication skills, are self-driven and deliver high quality results in a fast-paced environment.Technical Responsibilities:· Interact with various teams to develop an understanding of their security and safety requirements.· Apply the acquired knowledge to build tools find problems, or show the absence of security/safety problems.· Implement these tools through the use of SAT, SMT, BDDs, and various concepts from programming languages, theorem proving, formal verification and constraint solving.· Perform analysis of the customer systems using tools developed in-house or externally provided· Create software prototypes to verify and validate the devised solutions methodologies; integrate the prototypes into production systems using standard software development tools and methodologies.Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.